Quantization of residuals in video coding

ABSTRACT

A method of decoding an encoded stream into a reconstructed output signal is provided, the method comprising: receiving a first output signal decoded from a first base encoded stream according to a first codec; receiving a level encoded stream; decoding the level encoded stream to obtain a set of residuals; and, combining the set of residuals with the first output signal to generate a reconstructed signal, wherein the decoding the level encoded stream comprises: decoding a set of quantized coefficients from the level encoded stream; dequantizing the set of quantized coefficients.

BACKGROUND

A hybrid backward-compatible coding technology has been previously proposed, for example in WO 2014/170819 and WO 2018/046940, the contents of which are incorporated herein by reference.

A method is proposed therein which parses a data stream into first portions of encoded data and second portions of encoded data; implements a first decoder to decode the first portions of encoded data into a first rendition of a signal; implements a second decoder to decode the second portions of encoded data into reconstruction data, the reconstruction data specifying how to modify the first rendition of the signal; and applies the reconstruction data to the first rendition of the signal to produce a second rendition of the signal.

An addition is further proposed therein in which a set of residual elements is useable to reconstruct a rendition of a first time sample of a signal. A set of spatio-temporal correlation elements associated with the first time sample is generated. The set of spatio-temporal correlation elements is indicative of an extent of spatial correlation between a plurality of residual elements and an extent of temporal correlation between first reference data based on the rendition and second reference data based on a rendition of a second time sample of the signal. The set of spatio-temporal correlation elements is used to generate output data. As noted, the set of residuals are encoded to reduce overall data size.

Encoding applications have typically employed a quantization operation. By way of this compression process, in which each of one or more ranges of data values is compressed into a single value, allows the number of different values in a set of video data to be reduced, thereby rending that data more compressible. In this way, quantization schemes have been useful in some video for changing signals into quanta, so that certain variables can assume only certain discrete magnitudes. Typically a video codec divides visual data, in the form of a video frame, into discrete blocks, typically of a predetermined size or number of pixels.

A transform is then typically applied to the blocks so as to express the visual data in terms of sums of frequency components. That transformed data can then be pre-multiplied by a quantization scale code, and then subjected to division element-wise by the quantization matrix, with the output elements of the division of each transformed, pre-multiplied element by the matrix element, then being rounded. The treatment of different transformed elements with divisors, namely different elements of a quantization matrix, is typically used to allow for those frequency elements that have a greater impact upon visual appearance of the video to a viewer to be effectively allotted more data, or resolution, than less perceptible components.

Optimisations are sought to further reduce overall data size while balancing the objectives of not compromising the overall impression on the user once the signal has been reconstructed, and, optimising processing speed and complexity.

Quantization is a fundamental sub-process of substantially any encoding methods. In particular, quantization is the procedure of constraining values of signal properties (e.g., luminance of the pixels of a picture) from a substantially continuous set of values (such as the real numbers, or a high-bit-depth set of digital values, “Original Set”) to a relatively small discrete set (such as a finite and low-bit-depth set of integer values, “Quantized Set”).

An important aspect of quantization is the corresponding process of “inverse quantization” or “dequantization”, which is used to obtain from a quantized value (i.e., a value belonging to the small discrete set) a value belonging to the original (i.e., larger) set of values. Each value in the small discrete set actually corresponds to multiple (and potentially infinite) values in the larger set of values, so the key problem of dequantization is to define what of the multiple values should be chosen.

Traditional encoding methods are called to quantize values in the original set without knowing how those values are distributed from a statistical point of view. As a consequence, a typical decoding method is to select the value in the Original Set that is located approximately in the middle of the range defined in the Original Set by each quantized value in the Quantized Set. For instance, if Quantized value Q1 corresponds to the range (10, 19) in the Original Set, then a typical dequantizer would assign to Q1 the value 14. Clearly, if the dequantizer were to possess information indicating that 90% of the values of the Original Set that belonged to the range (10, 19) actually were either 10 or 11, then it would be advisable to make a different dequantization decision. Unfortunately, information on the statistical distribution of original values are typically not readily available in encoding methods used so far in the art.

Another important characteristic of quantization methods is the definition of each quantization range (i.e., the range of original values in the Original Set that corresponds to each quantized value in the Quantized Set). Again, in absence of known and reliable data pertaining the statistical distribution of values in the Original Set, most methods opt for uniform quantization steps, i.e., ranges of equal size.

In summary, methods of quantization and dequantization available in the art, due to their inability of being based on and/or driven by the statistical distribution of values in the Original Set, are inevitably sub-optimal in terms of both quality of the rendition of the signal (for lossy encoding) and compression performance.

SUMMARY OF THE INVENTION

In accordance with an aspect there is provided a method of encoding an input signal into a plurality of encoded streams, wherein the encoded streams may be combined to reconstruct the input signal, the method comprising: receiving an input signal; downsampling the input signal to create a downsampled signal; instructing an encoding of the downsampled signal using a base encoder to create a base encoded stream; instructing a decoding of the base encoded stream using a base decoder to generate a reconstructed signal; comparing the reconstructed signal to the input video to create a set of residuals; and, encoding the set of residuals to create a level encoded stream, including: applying a transform to the set of residuals to create a set of transformed coefficients; applying a quantization operation to the set of transformed coefficients to create a set of quantized coefficients; and applying an encoding operation to the quantized coefficients.

In accordance with another aspect, there is provided a method of decoding an encoded stream into a reconstructed output signal, the method comprising: receiving a first output signal decoded from a first base encoded stream according to a first codec; receiving a level encoded stream; decoding the level encoded stream to obtain a set of residuals; and, combining the set of residuals with the first output signal to generate a reconstructed signal, wherein the decoding the level encoded stream comprises: decoding a set of quantized coefficients from the level encoded stream; dequantizing the set of quantized coefficients. The combining may include combining with an upsampled version of the first output signal.

The level encoded stream may be a first level encoded stream; the set of quantized coefficients may be a first set of quantized coefficients; and, the set of residuals may be a first set of residuals, and wherein the method may further comprise: receiving a second level encoded stream; decoding the second level encoded stream to obtain a second set of residuals; and combining the second set of residuals with an upsampled version of the reconstructed signal to generate a reconstruction of an original resolution input signal, wherein decoding the second level encoded stream comprises: decoding a second set of quantized coefficients from the second level encoded stream; dequantizing the second set of quantized coefficients.

The method may advantageously allow the efficiency of the encoding and decoding process to be improved, by way of altering the degree and/or manner of compression applied to the coefficients in the quantization process in dependence on any of a number of factors based upon the video data to be coded. Thus the way in which the typically lossy procedure of quantization is performed during encoding a video stream can be adapted in such a way that an appropriate balance between encoding or compression efficient and visually perceptible compression of the input video, which is a relation that may vary greatly across different video frames and streams, may be applied depending upon the nature and content of the input video. This adaptable form of quantization may be used in cooperation with a dequantization process at a receiving decoder for instance, by way of signalling to the decoder the manner in which the quantizing has been performed, or the degree to which it has been altered from a default mode, for example, through transmission of parameters having values that represent or indicate that information.

A bin folding process may also be used to enhance the coding efficiency in some embodiments. In particular, in such cases, the quantization operation further comprises applying a bin folding operation to the set of coefficients, the bin folding operation comprising each coefficient that has a value in excess of a predetermined maximum value, the maximum value being defined by an upper limit of a first quantization bin of a plurality of quantization bins having a defined step width, being quantized so as to have a quantized value corresponding to the first quantization bin. This may be performed so as to place all residual or coefficient values that reside above a selected quantization bin into the selected bin. With regard to what may be understood as the endpoints of the range of values involved, the first bin may be considered to correspond to an upper value, or a bin corresponding to the highest (absolute) quantized value. Bin folding may be implemented at either or both of upper and lower endpoints. A similar process may be performed for the negative values in the range. The bin folding may be configured so as to adjust, or reduce, a bit rate based on at least one of network conditions and base stream processing. Thus the bin folding process may itself be configurable, for example with either or both of conditions and base stream processing or parameters derived therefrom being used to configure the bin folding, for example parameters defining the bin folding, in various embodiments.

In some embodiments the quantization operation further comprises using a dequantization offset. This may comprise applying the dequantization offset to a plurality of quantization bins having a defined step width to adjust each of the values to which one or more of the said plurality of quantization bins correspond by the value of the dequantization offset.

In such embodiments the value to which each of the plurality of quantization bins corresponds may be adjusted. Alternatively, either or both of the value corresponding to the start of the first bin of the plurality of bins and the value corresponding to the end of the last bin of the plurality of bins is not adjusted by, that is it remains unadjusted by, the value of the quantization offset. The first bin may be understood as corresponding to the numerically lowest value, or the minimum of the range. Likewise the last bin may be understood as representing the maximum of the range or the numerically greatest value. These adjustments and lack thereof may be applied in combination with quantization operations involving a dead zone and bin folding as described later in this disclosure.

Typically the value of the dequantization offset is adjustable, or configurable. The value of the dequantization offset may, in some embodiments, be varied based upon data indicating operating conditions under which the encoding is being performed

In some embodiments the method further comprises signalling the quantization offset value to a decoder at which the encoded stream is to be received. This signalling may for example be performed in implementations wherein the quantization offset value is dynamically varied during the encoding.

The dequantization operation typically comprises subtracting the dequantization offset value from a residual or coefficient value before quantization based on a quantization step width.

In some embodiments the value of the dequantization offset is adjusted based on a sign of the residual or coefficient. This may be effected so as to allow for symmetrical operations about a zero value.

The method may be performed such that, when the value of the dequantization offset is set to a first predetermined value, the application of the offset to the bin values is disabled. For instance this may be done by setting a quantization or dequantization offset value to zero.

In some embodiments the value of the dequantization offset is adjusted based on a defined width of a dead zone. In such embodiments, the quantization operation may be performed using a dead zone, as detailed later in disclosure. The dequantization offset can be configured or adjusted based on a defined width of the dead zone

In some embodiments the method comprises deriving, typically from a single reference dequantization offset value δ, corresponding to the signaled reference StepWidth for a given data layer, a respective dequantization offset value δ_(i) for each of the encoded coefficient groups. The offset value δ_(i) is typically dependent at least in part on one or both of the reference offset δ and on quantization StepWidth_(i) of set of coefficients i.

The offsets for all quantization step widths may, in some embodiments, be derived from a reference step width by according to the relationship:

Dequantization_Offsetk=f(StepWidth_(k)),

-   -   and the relationship:

Actual_Dequantization_Offset_(i)==Reference_Dequantization_Offset+f(StepWidth_(i))−f(Reference_StepWidth).

In such cases, typically f(StepWidth) is a logarithmic relationship.

In some embodiments the method may involve a stepwidth used in the quantization operation being varied in accordance with a stepwidth parameter. In particular, the step width may be varied for each of one or more of the coefficients in the set of coefficients, for instance for different coefficients within a 2×2 or 4×4 block of coefficients. For example, the step width may be varied such that a smaller step width value is used for one or more of the coefficients that are predetermined to influence perception of a decoded signal to a greater degree. The degree of influence is typically experimentally determined, whereby information may be obtained indicating which coefficients more heavily influence perception, to a viewer, of a decoded signal.

The step width is typically assigned a default value in accordance with a base stepwidth parameter. One or more modified step widths may be obtained in accordance with the base step width and a stepwidth modifier parameter. For example, this may be performed by the modified step width being obtained according to the formula modified_stepwidth=base_stepwidth*modifier, where the modifier may be set based on a particular coefficient within a block or unit. In such embodiments, a respective stepwidth modifier parameter may be used to modify the stepwidth used for each of one or more of the coefficients. In such embodiments, a respective stepwidth modifier parameter may be used to modify the stepwidth used for each of one or more of the coefficients.

A respective step width value can, in some embodiments, be used for, or associated with, each of two or more encoded streams, or levels of enhancement, comprising a base encoded stream and one or more enhancement level encoded stream.

In some embodiments the stepwidth modifier parameter is varied in dependence on a level of enhancement, that is, depending upon a level of enhancement employed. The stepwidth modifier may be varied such that a smaller step width is used for a first level encoded stream and a larger step width is used for a base encoded stream.

In some preferred embodiments the quantization operation uses a quantization matrix defined with a set of stepwidth modifier parameter values for different coefficients and different levels of enhancement. Thus the method may involve respective stepwidth modified parameter values for each coefficient and for each level of enhancement. The quantization matrix may be obtained by the encoder performing the method, and by a decoder performing a corresponding decoding process, by various means in different embodiments. In particular the quantization matrix may be preset at at least one of an encoder and a decoder, or wherein the quantization matrix may be signalled between an encoder and a decoder, and additionally or alternatively constructed dynamically at at least one of the encoder and decoder.

The method may further comprise constructing the quantization matrix as a function at least one of: one or more stored and one or more signalled parameters.

In some embodiments, scaled transform coefficients d[x][y], for x=0 . . . nTbS−1, y=0 . . . nTbS−1, and given quantization matrix qm[x][y], may be derived according to the formula:

d[x][y]=(TransformCoeffQ[x][y]*((qm[x+(levelIdxSwap*nTbS)][y]+stepWidthModifier[x][y])+appliedOffset[x][y]),

-   -   wherein TransformCoeffQ is an array of size (nTbS)×(nTbS)         containing entropy decoded quantized transform coefficients,     -   levelIdx is a variable specifying the index of an enhancement         sub-layer, and appliedOffset[x][y] and stepWidthModifier[x][y]         are variables. Moreover, appliedOffset[x][y] may correspond to a         dead zone as described elsewhere herein.

For example, levelIdx may be equal to 1 for enhancement sub-layer 1 and Ibe equal to 2 for enhancement sub-layer 2.

Typically, the variable stepWidthModifier[x][y] is derived according to:

-   -   if dequant_offset signalled_flag=0,         stepWidthModifier[x][y]=((((Floor(−Cconst*Ln(qm[x+(levelIdxSwap*nTbS)][y])))+Dconst)*(qm[x+(levelIdxSwap*nTbS)][y]2)))/32768),         wherein Cconst and Dconst are constants, and may have values or         5242 and 99614 respectively in one example.

In some embodiments the method comprises transmitting the plurality of encoded streams with signalling information indicating whether or not a dequantization offset is to be applied when performing a dequantization operation. Thus signalling information may be provided, indicating whether a quantization offset has been applied when performing the quantization operation, the dequantization offset corresponding to, or being equal to, the quantization offset in some embodiments. Typically the offset is at least a function of a quantization stepwidth.

In such cases, typically the offset is a function of a probability distribution of the values prior to quantization, additionally or alternatively to being a function of the stepwidth.

In some embodiments the method comprises applying a modification to a quantization stepwidth to generate a modified quantization stepwidth, and applying the modified quantization stepwidth for quantizing one or more coefficients. Preferably the modification is determined based on a probability distribution of the values prior to quantization.

Typically the quantization operation comprises quantizing the coefficients using a linear quantizer, wherein the linear quantizer uses a dead zone of variable size.

In such embodiments, the size of the dead zone may be set as a predetermined multiple of a stepwidth used in the quantization operation, for example as a linear function of a stepwidth value. Alternatively, a non-linear function of stepwidth value may be used. The size of a stepwidth used in the quantization operation is variable in some preferred embodiments, and the size of the dead zone is more preferably adapted in accordance with the variable stepwidth.

The size of the dead zone may be set by multiplying a stepwidth used in the quantization operation with a multiplier parameter, and wherein the value of the multiplier parameter is varied based upon data indicating operating conditions, e.g. available bit rates, under which the encoding is being performed. Thus in some embodiments the multiplier may also be adaptive.

In some embodiments the size of the dead zone is calculated as a multiple of quantization step width according to the formula Dead Zone=DZmultiplier×StepWidth, wherein the dead zone multiplier DZmultiplier is calculated as a linear function of StepWidth according to DZmultiplier=a*StepWidth+b, wherein a and b are constants, typically suitable constants that are preferably known to both the encoder and decoder respectively performing the method, and a corresponding decoding method.

The quantization operation is typically performed in accordance with one or more quantization parameters. The one or more quantization parameters are typically set to at least one of: control, and provide a desired bitrate in, one or more encoded streams. That is they may be set to control, and/or provide a desired bitrate in, one or more encoded streams. The desired bitrate is a common bitrate for all streams so as to generate a common encoded stream, or wherein different bitrates are provided for different encoded streams.

In such embodiments, preferably the one or more quantization parameters are set so as to provide a desired quality level, or to maximise a quality level, within a set of pre-defined bit-rate constraints. This may be understood as adjusting the parameters so as to provide a quality level that is as high, or great, as possible.

The method may comprise determining quantization parameters by receiving a status of a buffer that receives the one or more encoded streams and the base encoded stream; and using the status to determine the quantization parameters.

The buffer is preferably used to store and/or combine, an encoded base stream and an encoded enhancement stream, and is configured to receive inputs at variable bitrates while the output is read at a constant rate. A rate controller may read the status from the buffer so as to ensure that the buffer does not overflow or get empty, and that data are always available to be read at its output. A status of the buffer may also be used to generate the one or more quantization parameters. The one or more quantization parameters may be controlled based on the amount of data within the buffer. Typically the values of the quantization parameters are inversely related to the amount of data in the buffer.

In some embodiments quantization parameters are determined for each frame, residual and/or group of residuals, that is, for at least one of: each frame, residual, and group of residuals. Typically the quantization parameters for a frame are determined based on a target data size for the frame and a current data size for the frame using a previous set of quantization parameters. In any of these embodiments the quantization parameters may be based on a previous set of quantization parameters.

The method may comprise defining a set of curves to map a normalized size onto the one or more quantization parameters, wherein each curve comprises one or more of a multiplier and an offset that depends upon the properties of a current frame. A set of curves may be defined to map a normalised size onto a quantization parameter. Each curve may have one or more of a multiplier and an offset that may depend on the properties of a current frame (e.g. that may depend on a complexity of information to encode within the frame). The multiplier and the offset may define the shape of the curve. The multiplier may be applied to a size normalisation function that is a function of the quantization parameter Q. In one case, the current size (i.e. the size of frame t encoded with Q_(t-1)) and Q_(t-1) may be used to define a point within the space of the set of curves. This point may be used to select a set of closest curves from the set of curves. These may be a curve that is above the point and a curve that is below the point or a highest or lowest curve for the point. The set of closest curves may be used in an interpolation function together with the point to determine a new curve associated with the point. Once this new curve is determined, a multiplier and an offset for the new curve may be determined. These values may then be used together with the received target size to determine a value for Qt (e.g. the curve may define a function of size and Q). Thus typically the multiplier is applied to a size normalization function that is a function of the quantization parameter Q. The current size of a frame t encoded with Q_(t-1), and Q_(t-1), may be used to define a point within the space of the set of curves, and wherein the point is used to select a set of closest curves from the set of curves. The closest may be a curve that is above the point and a curve that is below the point or a highest or lowest curve for the point.

In such cases, the set of closest curves is typically used in an interpolation function together with the point to determine a new curve associated with the point, and a multiplier and an offset for the determined new curve may be determined, further comprising using the values of the multiplier and the offset for the determined new curve values together with a received target size to determine a value for Q_(t).

The set of curves may be stored in an accessible memory and updated based on a set of curves determined for a previous frame. In certain cases, adaptive quantization may be applied differently for different coefficient locations within a coding unit or block, e.g. for different elements in an array of 4 or 16 coefficients (for 2×2 or 4×4 transforms for instance).

In some embodiments, the quantization operation is performed using a quantization matrix, the quantization matrix being derived in accordance with an obtained value of a quantization matrix mode parameter. Preferably in such cases the quantization matrix mode parameter specifies the quantization matrix to be used in the encoding process.

Typically a respective, preferably different, quantization matrix is used for each of two or more levels of encoded streams. More preferably, different quantization matrices are used for each encoded stream, wherein a default quantization configuration is predetermined, and variations from the default configuration are signalled between the encoder and a decoder.

In these embodiments the method may comprise causing different quantization matrices to be used for respective encoded streams by way of at least one of: a default configuration, and causing a common quantization matrix to be used for the respective encoded streams by way of signalling to override the default configuration.

In some embodiments the quantization matrix is used for only a subset of, or only one of, a plurality of levels of enhancement. The quantization matrix is typically indexed by a position of the coefficient within a block in which the coefficients are arranged.

In some embodiments a base quantization matrix is defined with a set of values, and the base quantization matrix is modified in accordance with a scaling factor that is a function of a step width for one or more of the enhancement levels. The scaling factor may be computed as a clamped function of a stepwidth parameter, for example. In some embodiments each entry in the quantization matrix is scaled using an exponential function of the scaling factor.

In some embodiments a linear quantizer uses a non-centred dequantization offset. In particular the linear quantizer may use a dead zone of a different size with respect to the quantization step and non-centered dequantization offset.

Typically the value of the dequantization offset is received from an encoder. The received offset value offset may be added to a received quantized value prior to dequantization based on a stepwidth.

In accordance with another aspect there is provided a method of decoding an encoded stream into a reconstructed output video, the method comprising: receiving a first output signal decoded from a first base encoded stream according to a first codec; receiving a level encoded stream; decoding the level encoded stream to obtain a set of residuals; and, combining the set of residuals with the first output signal to generate a reconstructed signal, wherein the received encoded stream comprises a group of transform coefficients that have been scaled using a linear quantizer, and the decoding comprises applying a dequantization operation, wherein the dequantization operation is performed using a dead zone, wherein the size of the dead zone is obtained in accordance with a step width used in the dequantization operation and quantization parameters received from the encoder. The combining may include combining with an upsampled version of the first output signal.

In some preferred embodiments the method comprises determining from signalling information whether or not a dequantization offset is to be applied when performing the dequantization operation. The signalling information may be received, directly or indirectly, from an encoder. The information may be within a bitstream associated with the signal. The dequantization offset may be understood as that described earlier in this disclosure.

In such embodiments, typically the dequantization offset is at least a function of a quantization stepwidth. Preferably the dequantization offset is a function of a probability distribution of the values prior to quantization, additionally or alternatively to being a function of the stepwidth.

In some embodiments the method comprises applying a modification to a quantization stepwidth to generate a modified quantization stepwidth, and applying the modified quantization stepwidth for dequantizing one or more quantized values. In such embodiments, typically the modification is determined based on a probability distribution of the values prior to quantization. Preferably the method comprises encoding the input video, or the signal, as described in relation to the first aspect for instance, prior to the decoding. The method may comprise quantizing a value differently to the dequantizing.

The method may comprise encoding the signal prior to the decoding, the method comprising applying the quantization stepwidth to quantize one or more values to generate the one or more quantized values, such that quantization and dequantization is asymmetric.

In such embodiments involving asymmetric coding, the method may further comprise signalling the modification during encoding to a decoder or decoding operation.

Implementing encoder and decoder stages that are asymmetric may be affected (namely implemented) in a number of ways. In some cases this may both be in the broader encoder side, that is, the encoder at L1 and L2 may be asymmetric with the decoder that forms part of the L1 pathway.

The following sets out a plurality of further non-limiting implementations of asymmetric quantization and de-quantization, which may also be implemented in combination with the above described features relating to the foregoing aspects.

There is also provided a method of encoding an input video into a plurality of encoded streams and decoding at least one of the plurality of encoded streams, wherein the encoding comprises applying a first quantization operation using a first set of parameters and the decoding comprises asymmetrically applying a second dequantization using a different set of parameters from the first quantization operation. The decoding may be performed at a decoder or as part of a reconstruction pathway of the method of encoding.

There is also provided a method of (namely a method for) encoding an input video into a plurality of encoded streams and decoding at least one of the plurality of encoded streams, wherein the encoding comprises applying a first quantization operation and signalling a plurality of parameters to a decoding process and the decoding comprises receiving the plurality of parameters and applying a second dequantization using different parameters from the first quantization operation according to the received plurality of parameters.

There is also provided a method of (namely a method for) encoding an input video into a plurality of encoded streams, wherein the encoded streams may be combined to reconstruct the input video, the method comprising: receiving an input video, which is typically a full-resolution input video. The method may further comprise applying a quantization operation to a set of coefficients to create a set of quantized coefficients; and applying an encoding operation to the set of quantized coefficients, wherein applying the quantization operation comprises: adapting the quantization based on the first set of coefficients to be quantized, including varying a step-width used for different ones of the first set of coefficients, wherein the method further comprises signalling a modification parameter and wherein a first set of parameters derived from the adapting is signalled to a decoder to enable modification of dequantization of the first set quantized coefficient, wherein the modification parameter is used by a decoder to modify the first set of parameters such that dequantization parameters used are different quantization parameters used.

There is also provided a method of decoding an encoded stream into a reconstructed output video, the method comprising: retrieving a dequantization modification parameter; decoding a set of quantized coefficients from a first level encoded stream; obtaining a set of parameters indicating how to dequantize the first set of quantized coefficients; modifying the first set of parameters using the retrieved dequantization modification parameter; and dequantizing the set of quantized coefficients using the set of parameters, wherein different ones of the set of quantized coefficients are dequantized using respective dequantization parameters, such that dequantization and quantization are asymmetric.

In accordance with a further aspect there is provided an encoder for encoding an input video, the encoder being configured to perform the method according to the first aspect.

In accordance with a further aspect there is provided a decoder for decoding an encoded stream into a reconstructed output video, the decoder being configured to perform the method of according to the above aspect decoding aspects.

In accordance with a further aspect there is provided a system comprising an encoder and a decoder. The system may be adapted to perform asymmetric quantization as described earlier in this disclosure.

In accordance with a further aspect there is provided a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the methods.

More generally there is provided a method of encoding an input signal into a plurality of encoded streams. This method may be performed with any of the implementation details described above.

The video coding techniques described in this disclosure provide, with respect to conventional approaches, more efficient and effective quantization and dequantization in the context of tier-based hierarchical encoding methods. This may be understood in further view of the embodiments described below, which may be implemented with any of the above-described aspects.

Firstly, in this disclosure is may be understood that, in the context of tier-based coding formats, a signal is decomposed in multiple “echelons” (also known as “hierarchical tiers”) of data, each corresponding to a “Level of Quality” (“LoQ”) of the signal, from the highest echelon at the sampling rate of the original signal to a lowest echelon, which typically has a lower sampling rate than the original signal. In the non-limiting example when the signal is a picture, the lowest echelon may be a thumbnail of the original picture, or even just a single picture elements. Other echelons contain information on correction to apply to a reconstructed rendition in order to produce the final output. The decoded signal at a given Level of Quality is reconstructed by first decoding the lowest echelon (thus reconstructing the signal at the first—lowest—Level of Quality), then predicting a rendition of the signal at the second—next higher—Level of Quality, then decoding the corresponding second echelon of reconstruction data (also known as “residual data” at the second Level of Quality), then combining the prediction with the reconstruction data so as to reconstruct the rendition of the signal at the second—higher—Level of Quality, and so on, up to reconstructing the given Level of Quality.

Different echelons of data may be coded using different coding formats, and different Levels of Quality may have different sampling rates (e.g., resolutions, for the case of image or video signals). Subsequent echelons may refer to a same signal resolution (i.e., sampling rate) of the signal, or to a progressively higher signal resolution.

Methods and embodiments illustrated herein also include ways to suitably leverage predictable statistical characteristics of residual data in tier-based encoding methods. In particular, in the context of a tier-based hierarchy, residual data is distributed by construction according to known probability distribution patterns (e.g., by means of non-limiting example, laplacian distributions, poisson distributions, etc.): it is thus possible to rapidly identify key parameters said probability distributions and indicate them to a decoder by means of a relatively limited set of meta data.

At the same time, non-limiting embodiments herein also include ways to suitably adapt quantization and dequantization operations based on the known probability distribution of the original symbols, so as to improve at the same time compression efficiency, reconstruction quality and processing speed. Non-limiting embodiments herein also include methods to minimize signaling overhead by deriving with default formulas how the optimal dequantization offsets should be varied for each coefficient group based on the relative magnitude of their quantization step width.

Embodiments herein deviate with respect to conventional systems and methods, providing novel methods to dynamically adapt quantization and dequantization operations, based on suitable and concise indication of characteristics of the probability distribution of the original unquantized values.

According to its broadest aspect, the invention is a method of (namely a method for) rapidly assessing the probability distribution of original unquantized values, generating suitable signaling meta data based on said distribution and adapting quantization and dequantization operations based on said meta data. The invention also includes decoder-side methods to improve the statistical properties of dequantization offsets also in absence of signaling information from the encoder.

These approaches are especially valuable and effective in the context of tier-based encoding methods, since a tier-based hierarchy is produced in a way that implicitly defines the type of probability distribution of residual values, which in turn allows both to characterize each specific probability distribution with relatively few and quickly identifiable parameters and to assume a number of properties of dequantization offsets (such as for instance their relationship with the magnitude of quantization step widths). According to non-limiting embodiments described herein, the novel dequantization methods described herein are used in-loop within the encoding and decoding hierarchy of a tier-based encoding method.

For simplicity, non-limiting embodiments illustrated herein refer to a signal as a sequence of samples (i.e., two-dimensional images, video frames, video fields, sound frames, etc.). In the description the terms “image”, “picture” or “plane” (intended with the broadest meaning of “hyperplane”, i.e., array of elements with any number of dimensions and a given sampling grid) will be often used to identify the digital rendition of a sample of the signal along the sequence of samples, wherein each plane has a given resolution for each of its dimensions (e.g., X and Y), and comprises a set of plane elements (or “element”, or “pel”, or display element for two-dimensional images often called “pixel”, for volumetric images often called “voxel”, etc.) characterized by one or more “values” or “settings” (e.g., by ways of non-limiting examples, colour settings in a suitable colour space, settings indicating density levels, settings indicating temperature levels, settings indicating audio pitch, settings indicating amplitude, etc.). Each plane element is identified by a suitable set of coordinates, indicating the integer positions of said element in the sampling grid of the image. Signal dimensions can include only spatial dimensions (e.g., in the case of an image) or also a time dimension (e.g., in the case of a signal evolving over time).

As non-limiting examples, a signal can be an image, an audio signal, a multichannel audio signal, a telemetry signal, a video signal, a multi-view video signal (e.g., 3D video), a volumetric signal (e.g., medical imaging, scientific imaging, holographic imaging, etc.), a volumetric video signal, or even signals with more than four dimensions.

For simplicity, non-limiting embodiments illustrated herein often refer to signals that are displayed as 2D planes of settings (e.g., 2D images in a suitable colour space), such as for instance a video signal. The terms “frame” or “field” will be used interchangeably with the term “image”, so as to indicate a sample in time of the video signal: any concepts and methods illustrated for video signals made of frames (progressive video signals) can be easily applicable also to video signals made of fields (interlaced video signals), and vice versa. Despite the focus of embodiments illustrated herein on image and video signals, people skilled in the art can easily understand that the same concepts and methods are also applicable to any other types of multidimensional signal (e.g., audio signals, volumetric signals, stereoscopic video signals, 3DoF/6DoF video signals, plenoptic signals, etc.).

According to some embodiments described herein, a signal processor (e.g., computer processor hardware) is configured to receive data to quantize and a corresponding reference quantization step width to be used to quantize it. Based at least in part on the quantization step width, the signal processor identifies the range of unquantized values corresponding to quantized value zero (“Dead Zone”). The use of a dead zone is described earlier in this disclosure. In some non-limiting embodiments, the encoder calculates the Dead Zone from the reference step width based on a formula that is known to both encoder and decoder, which allows to vary the relative size of the Dead Zone quantization bin without requiring any signalling overhead. In a non-limiting embodiment, Dead Zone is calculated as a multiple of quantization step width (i.e., Dead Zone=DZmultiplier*StepWidth), wherein the DeadZone multiplier DZmultiplier is calculated as a linear function of StepWidth (i.e., DZmultiplier=a*StepWidth+b, wherein a and b are suitable constants known to both encoder and decoder). In other non-limiting embodiments, the relationship between DZmultiplier is non-linear. In some non-limiting embodiments, DZmultiplier is represented with a fixed-point integer number, so as to avoid using hardware-specific floating point computations.

According to some further embodiments described herein, the encoder, based at least in part on a metric calculated on data to quantize, identifies a dequantization offset value δ to add to the center-most extreme of the each range of unquantized values (“Quantization Bin”) in order to obtain the dequantized value (R_i){circumflex over ( )} corresponding to Qi. Dequantization of a quantized symbol Q is performed by both encoder (for simulated decoding) and decoder as follows.

If the quantized symbol Qi is positive:

R _(i) =Q _(i)·(StepWidth·QuantizationMatrix)+(δ−DeadZone)

If instead the symbol Qi is negative:

R _(i) =Q _(i)·(StepWidth·QuantizationMatrix)−(δ−DeadZone)

In some further embodiments, the decoder produces dequantization offset value δ by analyzing the signal encoded bytestream. If a signaling bit corresponding to dequantization offset is set to 1, the decoder processes the following byte in the bytestream so as to produce a reference dequantization offset.

According to some further embodiments, for a plurality of sets of encoded data (e.g., groups of encoded transformed coefficients) the encoder signals to the decoder a single parameter corresponding to a reference quantization StepWidth, and the decoder leverages a quantization matrix to process the parameter corresponding to the reference StepWidth and derive a distinct quantization StepWidth for each of the groups of transformed coefficients. In order to limit the number of dequantization offsets to be transmitted as metadata in the bytestream, both encoder and decoder automatically derive from a single reference dequantization offset value δ—intended as the offset corresponding to the signaled reference StepWidth for the data layer—to a specific dequantization offset value δi for each of the encoded coefficient groups, said offset value δi being dependent at least in part on signaled dequantization offset δ and on quantization StepWidthi of set of coefficients i.

In some embodiments, the encoder and the decoder calculate offsets for all quantization StepWidths derived from the reference StepWidth by means of assuming a relationship as follows:

Dequantization_Offsetk=f(StepWidthk)

And consequently:

Actual_Dequantization_Offseti==Reference_Dequantization_Offset+f(StepWidthi)−f(Reference_StepWidth)

In some embodiments, f(StepWidth) is a logarithmic relationship.

In other embodiments, the encoder derives a value for reference dequantization offset value δ by means of an RDO (“Rate Distorsion Optimization”) algorithm. By way of non-limiting example, in an embodiment the encoder calculates the reference dequantization offset value δ by performing a binary search optimization algorithm on the data to quantize, using a suitable metric (e.g., by way of non-limiting example, MSE, MAE, PSNR) to determine the fidelity of the rendition produced by dequantizing with a given dequantization offset value δ. In a non-limiting embodiment, the signal processor selects a value that minimizes the mean square error between original unquantized values and dequantized value (R_i){circumflex over ( )}. In other non-limiting embodiments, the encoder performs said optimization taking into account only residuals that correspond to areas of the signal that have been identified of relatively high priority, according to a suitable priority map.

In other embodiments, the encoder selects a dequantization offset and detects coefficients values that are likely to be quantized far from the dequantization point of the corresponding quantization bucket. Accordingly, the encoder assigns to said coefficients the quantized symbol of a neighbouring quantization bucket, so as to reduce a metric of difference between the unquantized value of the coefficient and the dequantized value of the quantized symbol assigned to the coefficient. In a non-limiting embodiment, the encoder detects at least one coefficient pertaining to an edge and assigns to it a different quantized symbol, so as to enhance the edge transition.

In further embodiments, the encoder performs quantization in two steps: a first-pass quantization with relatively large stepwidth and relatively small dead zone, with the purpose of grouping neighbouring residual values in clusters, subsequently, the actual quantization with relatively small target quantization stepwidths, detecting a reference dequantization offset for the coefficient group.

In some embodiments, the decoder is configured to generate a progressively larger dequantization offset as the quantization bucket moves from the center of the residual distribution (zero symbol/dead zone) to the two tails of the distribution. In some non-limiting embodiments, the decoder produces for each quantization StepWidthi a StepWidth_Modifieri value dependent on StepWidthi. The decoder then produces Decoding_StepWidthi=(StepWidthi+StepWidth_Modifieri). Decoding_StepWidthi is then used instead of StepWidthi in order to perform dequantization.

In some embodiments, the encoder signals dequantization offset value δ as encoding metadata within the encoded bitstream. In an embodiment, a decoder detects whether or not a dequantization offset is signalled for a given grouping of quantized coefficients by means of a given signaling bit in the bytestream: if the bit is set to 1, the following byte contains information corresponding to the magnitude of the reference dequantization offset. In other non-limiting embodiments, when no dequantization offset is signaled, the decoder proceeds with calculating a StepWidth_Modifier for each group of transformed coefficient. In other non-limiting embodiments, based on a second signaling bit, the decoder processes the bytestream to produce a reference dequantization offset, and at the same time produces one or more StepWidth_Modifiers to be added to the StepWidth in order to progressively increase the dequantization offset. The StepWidth_Modifier may be useful to avoid artefacts such as mosquito noise.

In some embodiments, processing operations and intermediate variables are performed by means of fixed-point integer numbers, avoiding to perform processing operations dependent on floating point numbers.

In some further non-limiting embodiments, the offset values δi for each quantization bin are produced based on parameters identifying the probability distribution of unquantized values. In a non-limiting embodiment, offset values are calculated by assuming a laplacian (i.e., double-exponential) probability distribution, identified based at least in part on the meta data. In other non-limiting embodiments, offset values are directly decoded from the meta data. In a non-limiting embodiment, the offset value δ is the same for all bins except for the bin centered around the zero value, said offset value δ is added to the center-most edge of the quantization bin for bins corresponding to positive unquantized values (thus reducing the magnitude of the dequantized value with respect to the average of the range of the bin) and subtracted to the average of the bin for bins corresponding to negative unquantized values (thus again reducing the absolute value of the dequantized value with respect to the average of the range of the bin).

In other embodiments, the signal processor is configured to implement a stochastic dequantization process (“statistical dithering”, or “statistical dequantization”), reproducing for dequantized values belonging to a same Quantization Bin i a probability distribution similar to that of the original unquantized values that were quantized into that Bin. The signal processor receives meta data indicating a probability distribution for the original unquantized values. Based at least in part on parameters of said probability distribution, on the smallest and largest value of each Bin and on a random-generated value, the signal processor produces a dequantized value (R_i){circumflex over ( )} for each quantized value Qi. In such embodiments, multiple occurrences of a same quantized value Qi may be dequantized into multiple different dequantized values (R_i){circumflex over ( )}. In a non-limiting embodiment, parameters in the meta data indicating the probability distribution for the original unquantized values are assumed to be parameters of a laplacian distribution.

In other embodiments, the signal processor processes the received meta data corresponding to quantized data and produces a parameter StepWidthi indicating the size of a Quantization Bin and information corresponding to the probability distribution of the original unquantized data. Based at least in part on said parameter and said information, the signal processor produces the ranges of all of the Quantization Bins to be used to dequantize the received quantized data. In a non-limiting embodiment, the size of the Quantization Bins is non-uniform. In other non-limiting embodiments, the size of the Quantization Bins is non-uniform, and is obtained based at least in part on a lookup table known to the signal processor. In other non-limiting embodiment, dequantization follows a scheme of Dead Zone plus Uniform Step Width (DZ+USW) wherein both DZ and USW are functions of parameter StepWidthi. In a non-limiting embodiment, the dequantization offset is also a function of parameter StepWidth, and calculated as f(StepWidth).

According to other embodiments, the signal processor pre-calculates a set of parameters based on the received meta data and stores said parameters in a lookup table, so as to speed up the process of dequantization of quantized data.

According to some embodiments, information corresponding to the probability distribution of original unquantized values comprises a parameter corresponding to the diversity parameter (“b”) of a laplacian distribution. In other non-limiting embodiments, information corresponding to the probability distribution of original unquantized values comprises parameters corresponding to the probabilities of some Quantization Bins (“Mini Histogram”).

In some embodiments, the quantized data corresponds to residual data in the context of a tier-based (hierarchical) decoding method. A signal processor configured as a decoder decodes a rendition of the signal at a first (lower) level of quality. The decoder then processes the rendition of the signal at the first level of quality to produce a preliminary rendition of the signal at a second (higher) level of quality. The decoder then decodes the received quantized data according to embodiments herein and produces dequantized reconstruction data. Based at least in part on said dequantized reconstruction data, the decoder produces residual data. The decoder then combines said residual data with the preliminary rendition of the signal at the second level of quality, producing a rendition of the signal at the second level of quality.

Some embodiments include a signal processor configured to produce a set of values (“quantized data”), said set of quantized data corresponding to a difference between a set of elements of a signal at a given higher level of quality and a corresponding predicted signal at the same level of quality, the method comprising, implemented within an encoder: Receiving a rendition of the signal at the higher level of quality; Producing a rendition of the signal at the lower level of quality; Producing a predicted rendition of the signal at a lower level of quality; Producing a set of residual data corresponding to a difference between the rendition of the signal at the lower level of quality and the predicted rendition of the signal at the lower level of quality; Based at least in part on the set of residual data, producing a set of unquantized reconstruction data; Quantizing the set of unquantized reconstruction data according to non-limiting methods described herein, producing quantized data and corresponding dequantization meta data; Processing the set of quantized data and the corresponding dequantization meta data according to non-limiting dequantization methods described herein, producing dequantized reconstruction data; Processing dequantized reconstruction data, producing dequantized residual data; Combining dequantized residual data with the predicted rendition of the signal at the lower level of quality, producing a reconstructed rendition of the signal at the lower level of quality; Processing the reconstructed rendition of the signal at the lower level of quality so as to produce a predicted rendition of the signal at the higher level of quality; Producing a set of residual data corresponding to a difference between the rendition of the signal at the higher level of quality and the predicted rendition of the signal at the higher level of quality; Based at least in part on the set of residual data, producing a set of unquantized reconstruction data; Quantizing the set of unquantized reconstruction data according to non-limiting methods described herein, producing quantized data and corresponding dequantization meta data.

According to a further aspect, the decoder and encoder may be asymmetric. That is, a decoding process may use different or modified parameters to a corresponding encoding process. This applies to a decoding operation performed in a method of encoding and a decoding operation performed in a method of decoding. In examples, a quantization operation performed during an encoding operation may use a step-width formula wherein a quantization operation performed during a decoding operation may modify or modulate the step-width formula. The following sets out a plurality of further non-limiting examples, aspects and implementations of asymmetric quantization and de-quantization.

According to a further aspect of the invention, there is provided a method of encoding an input video into a plurality of encoded streams and decoding at least one of the plurality of encoded streams, wherein the encoding comprises applying a first quantization operation using a first set of parameters and the decoding comprises asymmetrically applying a second dequantization using a different set of parameters from the first quantization operation. The decoding may be performed at a decoder or as part of a reconstruction pathway of the method of encoding.

According to a further aspect of the invention, there is provided a method of encoding an input video into a plurality of encoded streams and decoding at least one of the plurality of encoded streams, wherein the encoding comprises applying a first quantization operation and signalling a plurality of parameters to a decoding process and the decoding comprises receiving the plurality of parameters and applying a second dequantization using different parameters from the first quantization operation according to the received plurality of parameters.

According to a further aspect of the invention, there is provided a method of encoding an input video into a plurality of encoded streams, wherein the encoded streams may be combined to reconstruct the input video, the method comprising: receiving an input video, which is typically a full-resolution input video; downsampling the input video to create a downsampled video; instructing an encoding of the downsampled video using a base encoder to create a base encoded stream; instructing a decoding of the base encoded stream using a base decoder to generate a reconstructed video; comparing the reconstructed video to the downsampled video to create a first set of residuals; and, encoding the first set of residuals to create a first level encoded stream, including: applying a transform to the first set of residuals to create a first set of coefficients; applying a quantization operation to the first set of coefficients to create a first set of quantized coefficients; and applying an encoding operation to the first set of quantized coefficients, wherein applying the quantization operation comprises: adapting the quantization based on the first set of coefficients to be quantized, including varying a step-width used for different ones of the first set of coefficients, wherein the method further comprises signalling a modification parameter and wherein a first set of parameters derived from the adapting is signalled to a decoder to enable modification of dequantization of the first set quantized coefficient, wherein the modification parameter is used by a decoder to modify the first set of parameters such that dequantization parameters used are different quantization parameters used.

According to a further aspect, there may be provided a method of (namely a method for) decoding an encoded stream into a reconstructed output video, the method comprising: retrieving a dequantization modification parameter; receiving a first base encoded stream; instructing a decoding operation on the first base encoded stream using a base decoder to generate a first output video; receiving a first level encoded stream; decoding the first level encoded stream to obtain a first set of residuals; and, combining the first set of residuals with the first output video to generate a reconstructed video, wherein decoding the first level encoded stream comprises: decoding a first set of quantized coefficients from the first level encoded stream; obtaining a first set of parameters indicating how to dequantize the first set of quantized coefficients; modifying the first set of parameters using the retrieved dequantization modification parameter; and dequantizing the first set of quantized coefficients using the first set of parameters, wherein different ones of the first set of quantized coefficients are dequantized using respective dequantization parameters, such that dequantization and quantization are asymmetric.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 shows a high-level schematic of an encoding process;

FIG. 2 shows a high-level schematic of a decoding process;

FIG. 3 shows a high-level schematic of an encoding process and specific encoding steps;

FIG. 4 shows a high-level schematic of a decoding process and specific decoding steps;

FIG. 5 shows a high-level schematic of an encoding process;

FIG. 6 shows a high-level schematic of a further decoding process;

FIG. 7 shows a flowchart of concepts described herein;

FIGS. 8A-8D show how the quantization in an encoding process may be performed according to specific examples;

FIG. 9 shows an example residual distribution;

FIG. 10 shows an example of the generation and use of dequantization modifiers;

FIG. 11 shows examples of dequantization offsets;

FIG. 12 shows an further example diagram illustrating examples of dequantization offsets, similar to FIG. 11; and,

FIG. 13 shows a block diagram of an example computer system that provides data processing according to example methods;

FIG. 14 is a schematic illustration of an encoder; and,

FIG. 15 is a schematic illustration of a decoder.

DETAILED DESCRIPTION

The present invention relates to methods. In particular, the present invention relates to methods for encoding and decoding signals. Processing data may include, but is not limited to, obtaining, deriving, outputting, receiving and reconstructing data.

The coding technology discussed herein is a flexible, adaptable, highly efficient and computationally inexpensive coding format which combines a video coding format, a base codec, (e.g. AVC, HEVC, or any other present or future codec) with an enhancement level of coded data, encoded using a different technique. The technology uses a down-sampled source signal encoded using a base codec to form a base stream. An enhancement stream is formed using an encoded set of residuals which correct or enhance the base stream for example by increasing resolution or by increasing frame rate. There may be multiple levels of enhancement data in a hierarchical structure. In certain arrangements, the base stream may be decoded by a hardware decoder while the enhancement stream may be suitable for a software implementation.

It is important that any optimisation used in the new coding technology is tailored to the specific requirements or constraints of the enhancement stream and is of low complexity. Such requirements or constraints include: the potential reduction in computational capability resulting from the need for software decoding of the enhancement stream; the need for combination of a decoded set of residuals with a decoded frame; the likely structure of the residual data, i.e. the relatively high proportion of zero values with highly variable data values over a large range; the nuances of a quantized block of coefficients; and, the structure of the enhancement stream being a set of discrete residual frames separated into various components. Note that the constraints placed on the enhancement stream mean that a simple and fast entropy coding operation is essential to enable the enhancement stream to effectively correct or enhance individual frames of the base decoded video. Note that in some scenarios the base stream is also being decoded substantially simultaneously before combination, putting a strain on resources.

In one case, the methods described herein may be applied to so-called planes of data that reflect different colour components of a video signal. For example, the methods described herein may be applied to different planes of YUV or RGB data reflecting different colour channels. Different colour channels may be processed in parallel. Hence, references to sets of residuals as described herein may comprise multiple sets of residuals, where each colour component has a different set of residuals that form part of a combined enhancement stream. The components of each stream may be collated in any logical order, for example, each plane at the same level may be grouped and sent together or, alternatively, the sets of residuals for different levels in each plane may be sent together.

This present document preferably fulfils the requirements of the following ISO/IEC documents: “Call for Proposals for Low Complexity Video Coding Enhancements” ISO/IEC JTC1/SC29/WG11 N17944, Macao, CN, October 2018 and “Requirements for Low Complexity Video Coding Enhancements” ISO/IEC JTC1/SC29/WG11 N18098, Macao, CN, October 2018 (which are incorporated by reference herein).

The general structure of the proposed encoding scheme in which the presently described techniques can be applied, uses a down-sampled source signal encoded with a base codec, adds a first level of correction data to the decoded output of the base codec to generate a corrected picture, and then adds a further level of enhancement data to an up-sampled version of the corrected picture. Thus, the streams are considered to be a base stream and an enhancement stream. This structure creates a plurality of degrees of freedom that allow great flexibility and adaptability to many situations, thus making the coding format suitable for many use cases including Over-The-Top (OTT) transmission, live streaming, live Ultra High Definition (UHD) broadcast, and so on. Although the decoded output of the base codec is not intended for viewing, it is a fully decoded video at a lower resolution, making the output compatible with existing decoders and, where considered suitable, also usable as a lower resolution output. In certain cases, a base codec may be used to create a base stream. The base codec may comprise an independent codec that is controlled in a modular or “black box” manner. The methods described herein may be implemented by way of computer program code that is executed by a processor and makes function calls upon hardware and/or software implemented base codecs.

In general, the term “residuals” as used herein refers to a difference between a value of a reference array or reference frame and an actual array or frame of data. The array may be a one or two-dimensional array that represents a coding unit. For example, a coding unit may be a 2×2 or 4×4 set of residual values that correspond to similar sized areas of an input video frame. It should be noted that this generalised example is agnostic as to the encoding operations performed and the nature of the input signal. Reference to “residual data” as used herein refers to data derived from a set of residuals, e.g. a set of residuals themselves or an output of a set of data processing operations that are performed on the set of residuals. Throughout the present description, generally a set of residuals includes a plurality of residuals or residual elements, each residual or residual element corresponding to a signal element, that is, an element of the signal or original data. The signal may be an image or video. In these examples, the set of residuals corresponds to an image or frame of the video, with each residual being associated with a pixel of the signal, the pixel being the signal element. Examples disclosed herein describe how these residuals may be modified (i.e. processed) to impact the encoding pipeline or the eventually decoded image while reducing overall data size. Residuals or sets may be processed on a per residual element (or residual) basis, or processed on a group basis such as per tile or per coding unit where a tile or coding unit is a neighbouring subset of the set of residuals. In one case, a tile may comprise a group of smaller coding units. Note that the processing may be performed on each frame of a video or on only a set number of frames in a sequence.

In general, each or both enhancement streams may be encapsulated into one or more enhancement bitstreams using a set of Network Abstraction Layer Units (NALUs). The NALUs are meant to encapsulate the enhancement bitstream in order to apply the enhancement to the correct base reconstructed frame. The NALU may for example contain a reference index to the NALU containing the base decoder reconstructed frame bitstream to which the enhancement has to be applied. In this way, the enhancement can be synchronised to the base stream and the frames of each bitstream combined to produce the decoded output video (i.e. the residuals of each frame of enhancement level are combined with the frame of the base decoded stream). A group of pictures may represent multiple NALUs.

Returning to the initial process described above, where a base stream is provided along with two levels (or sub-levels) of enhancement within an enhancement stream, an example of a generalised encoding process is depicted in the block diagram of FIG. 1. An input full resolution video 100 is processed to generate various encoded streams 101, 102, 103. A first encoded stream (encoded base stream) is produced by feeding a base codec (e.g., AVC, HEVC, or any other codec) with a down-sampled version of the input video. The encoded base stream may be referred to as the base layer or base level. A second encoded stream (encoded level 1 stream) is produced by processing the residuals obtained by taking the difference between a reconstructed base codec video and the down-sampled version of the input video. A third encoded stream (encoded level 2 stream) is produced by processing the residuals obtained by taking the difference between an up-sampled version of a corrected version of the reconstructed base coded video and the input video. In certain cases, the components of FIG. 1 may provide a general low complexity encoder. In certain cases, the enhancement streams may be generated by encoding processes that form part of the low complexity encoder and the low complexity encoder may be configured to control an independent base encoder and decoder (e.g. as packaged as a base codec). In other cases, the base encoder and decoder may be supplied as part of the low complexity encoder. In one case, the low complexity encoder of FIG. 1 may be seen as a form of wrapper for the base codec, where the functionality of the base codec may be hidden from an entity implementing the low complexity encoder.

A down-sampling operation illustrated by downsampling component 105 may be applied to the input video to produce a down-sampled video to be encoded by a base encoder 113 of a base codec. The down-sampling can be done either in both vertical and horizontal directions, or alternatively only in the horizontal direction.

The base encoder 113 and a base decoder 114 may be implemented by a base codec (e.g. as different functions of a common codec). The base codec, and/or one or more of the base encoder 113 and the base decoder 114 may comprise suitably configured electronic circuitry (e.g. a hardware encoder/decoder) and/or computer program code that is executed by a processor.

Each enhancement stream encoding process may not necessarily include an up-sampling step. In FIG. 1 for example, the first enhancement stream is conceptually a correction stream while the second enhancement stream is up-sampled to provide a level of enhancement.

Looking at the process of generating the enhancement streams in more detail, to generate the encoded Level 1 stream, the encoded base stream is decoded by the base decoder 114 (i.e. a decoding operation is applied to the encoded base stream to generate a decoded base stream). Decoding may be performed by a decoding function or mode of a base codec. The difference between the decoded base stream and the down-sampled input video is then created at a level 1 comparator 110 (i.e. a subtraction operation is applied to the down-sampled input video and the decoded base stream to generate a first set of residuals). The output of the comparator 110 may be referred to as a first set of residuals, e.g. a surface or frame of residual data, where a residual value is determined for each picture element at the resolution of the base encoder 113, the base decoder 114 and the output of the downsampling block 105.

The difference is then encoded by a first encoder 115 (i.e. a level 1 encoder) to generate the encoded Level 1 stream 102 (i.e. an encoding operation is applied to the first set of residuals to generate a first enhancement stream).

As noted above, the enhancement stream may comprise a first level of enhancement 102 and a second level of enhancement 103. The first level of enhancement 102 may be considered to be a corrected stream, e.g. a stream that provides a level of correction to the base encoded/decoded video signal at a lower resolution than the input video 100. The second level of enhancement 103 may be considered to be a further level of enhancement that converts the corrected stream to the original input video 100, e.g. that applies a level of enhancement or correction to a signal that is reconstructed from the corrected stream.

In the example of FIG. 1, the second level of enhancement 103 is created by encoding a further set of residuals. The further set of residuals are generated by a level 2 comparator 119. The level 2 comparator 119 determines a difference between an up-sampled version of a decoded level 1 stream, e.g. the output of an upsampling component 117, and the input video 100. The input to the upsampling component 117 is generated by applying a first decoder (i.e. a level 1 decoder) to the output of the first encoder 115. This generates a decoded set of level 1 residuals. These are then combined with the output of the base decoder 114 at summation component 120. This effectively applies the level 1 residuals to the output of the base decoder 114. It allows for losses in the level 1 encoding and decoding process to be corrected by the level 2 residuals. The output of summation component 120 may be seen as a simulated signal that represents an output of applying level 1 processing to the encoded base stream 101 and the encoded level 1 stream 102 at a decoder.

As noted, an up-sampled stream is compared to the input video which creates a further set of residuals (i.e. a difference operation is applied to the up-sampled re-created stream to generate a further set of residuals). The further set of residuals are then encoded by a second encoder 121 (i.e. a level 2 encoder) as the encoded level 2 enhancement stream (i.e. an encoding operation is then applied to the further set of residuals to generate an encoded further enhancement stream).

Thus, as illustrated in FIG. 1 and described above, the output of the encoding process is a base stream 101 and one or more enhancement streams 102, 103 which preferably comprise a first level of enhancement and a further level of enhancement. The three streams 101, 102 and 103 may be combined, with or without additional information such as control headers, to generate a combined stream for the video encoding framework that represents the input video 100. It should be noted that the components shown in FIG. 1 may operate on blocks or coding units of data, e.g. corresponding to 2×2 or 4×4 portions of a frame at a particular level of resolution. The components operate without any inter-block dependencies, hence they may be applied in parallel to multiple blocks or coding units within a frame. This differs from comparative video encoding schemes wherein there are dependencies between blocks (e.g. either spatial dependencies or temporal dependencies). The dependencies of comparative video encoding schemes limit the level of parallelism and require a much higher complexity.

A corresponding generalised decoding process is depicted in the block diagram of FIG. 2. FIG. 2 may be said to show a low complexity decoder that corresponds to the low complexity encoder of FIG. 1. The low complexity decoder receives the three streams 101, 102, 103 generated by the low complexity encoder together with headers 204 containing further decoding information. The encoded base stream 101 is decoded by a base decoder 210 corresponding to the base codec used in the low complexity encoder. The encoded level 1 stream 102 is received by a first decoder 211 (i.e. a level 1 decoder), which decodes a first set of residuals as encoded by the first encoder 115 of FIG. 1. At a first summation component 212, the output of the base decoder 210 is combined with the decoded residuals obtained from the first decoder 211. The combined video, which may be said to be a level 1 reconstructed video signal, is upsampled by upsampling component 213. The encoded level 2 stream 103 is received by a second decoder 214 (i.e. a level 2 decoder). The second decoder 214 decodes a second set of residuals as encoded by the second encoder 121 of FIG. 1. Although the headers 204 are shown in FIG. 2 as being used by the second decoder 214, they may also be used by the first decoder 211 as well as the base decoder 210. The output of the second decoder 214 is a second set of decoded residuals. These may be at a higher resolution to the first set of residuals and the input to the upsampling component 213. At a second summation component 215, the second set of residuals from the second decoder 214 are combined with the output of the upsampling component 213, i.e. an upsampled reconstructed level 1 signal, to reconstruct decoded video 250.

As per the low complexity encoder, the low complexity decoder of FIG. 2 may operate in parallel on different blocks or coding units of a given frame of the video signal. Additionally, decoding by two or more of the base decoder 210, the first decoder 211 and the second decoder 214 may be performed in parallel. This is possible as there are no inter-block dependencies.

In the decoding process, the decoder may parse the headers 204 (which may contain global configuration information, picture or frame configuration information, and data block configuration information) and configure the low complexity decoder based on those headers. In order to re-create the input video, the low complexity decoder may decode each of the base stream, the first enhancement stream and the further or second enhancement stream. The frames of the stream may be synchronised and then combined to derive the decoded video 250. The decoded video 250 may be a lossy or lossless reconstruction of the original input video 100 depending on the configuration of the low complexity encoder and decoder. In many cases, the decoded video 250 may be a lossy reconstruction of the original input video 100 where the losses have a reduced or minimal effect on the perception of the decoded video 250.

In each of FIGS. 1 and 2, the level 2 and level 1 encoding operations may include the steps of transformation, quantization and entropy encoding (e.g. in that order). Similarly, at the decoding stage, the residuals may be passed through an entropy decoder, a dequantizer and an inverse transform module (e.g. in that order). Any suitable encoding and corresponding decoding operation may be used. Preferably however, the level 2 and level 1 encoding steps may be performed in software (e.g. as executed by one or more central or graphical processing units in an encoding device).

The transform as described herein may use a directional decomposition transform such as a Hadamard-based transform. Both may comprise a small kernel or matrix that is applied to flattened coding units of residuals (i.e. 2×2 or 4×4 blocks of residuals). More details on the transform can be found for example in patent applications PCT/EP2013/059847 or PCT/GB2017/052632, which are incorporated herein by reference. The encoder may select between different transforms to be used, for example between a size of kernel to be applied.

The transform may transform the residual information to four surfaces. For example, the transform may produce the following components: average, vertical, horizontal and diagonal. As alluded to earlier in this disclosure, these components that are output by the transform may be taken in such embodiments as the coefficients to be quantized in accordance with the described methods.

In summary, the methods and apparatuses herein are based on an overall approach which is built over an existing encoding and/or decoding algorithm (such as MPEG standards such as AVC/H.264, HEVC/H.265, etc. as well as non-standard algorithm such as VP9, AV1, and others) which works as a baseline for an enhancement layer which works accordingly to a different encoding and/or decoding approach. The idea behind the overall approach of the examples is to hierarchically encode/decode the video frame as opposed to the use block-based approaches as used in the MPEG family of algorithms. Hierarchically encoding a frame includes generating residuals for the full frame, and then a decimated frame and so on.

The video compression residual data for the full-sized video frame may be referred to as LoQ-2 (e.g. 1920×1080 for an HD video frame or higher for a UHD frame), while that of the decimated frame may be referred to as LoQ-x, where x denotes a number corresponding to a hierarchical decimation. In the described examples of FIGS. 1 and 2, the variable x may have values of 1 and 2 represent the first and second enhancement streams. Hence there are 2 hierarchical levels for which compression residuals will be generated. Other naming schemes for the levels may also be applied without any change in functionality (e.g. the level 1 and level 2 enhancement streams described herein may alternatively be referred to as level 1 and level 2 streams—representing a count down from the highest resolution).

A more detailed encoding process is depicted in the block diagram of FIG. 3. The encoding process is split into two halves as shown by the dashed line. Below the dashed line is the base level of an encoder 300, which may usefully be implemented in hardware or software. Above the dashed line is the enhancement level, which may usefully be implemented in software. The encoder 300 may comprise only the enhancement level processes, or a combination of the base level processes and enhancement level processes as needed. The encoder 300 may usefully be implemented in software, especially at the enhancement level. This arrangement allows, for example, a legacy hardware encoder that provides the base level to be upgraded using a firmware (e.g. software) update, where the firmware is configured to provide the enhancement level. In newer devices, both the base level and the enhancement level may be provided in hardware and/or a combination of hardware and software.

The encoder topology at a general level is as follows. The encoder 300 comprises an input I for receiving an input signal 30. The input signal 30 may comprise an input video signal, where the encoder is applied on a frame-by-frame basis. The input I is connected to a down-sampler 305D and processing block 300-2. The down-sampler 305D may correspond to the downsampling component 105 of FIG. 1 and the processing block 300-2 may correspond to the second encoder 121 of FIG. 1, The down-sampler 305D outputs to a base codec 320 at the base level of the encoder 300. The base codec 320 may implement the base encoder 113 and the base decoder 114 of FIG. 1. The down-sampler 305D also outputs to processing block 300-1. The processing block 300-1 may correspond to the first encoder 115 of FIG. 1. Processing block 300-1 passes an output to an up-sampler 305U, which in turn outputs to the processing block 300-2. The upsampler 305U may correspond to the upsampling component 117 of FIG. 1. Each of the processing blocks 300-2 and 300-1 comprise one or more of the following modules: a transform block 310, a quantization block 320, an entropy encoding block 330 and a residual processing block 350. The residual block 350 may occur prior to the transform block 310 and/or control residual processing in the processing blocks 300. The order of processing may be as set out in the Figures.

The input signal 30, such as in this example a full (or highest) resolution video, is processed by the encoder 300 to generate various encoded streams. A base encoded stream is produced by feeding the base codec 320 (e.g., AVC, HEVC, or any other codec) at the base level with a down-sampled version of the input video 30, using the down-sampler 305D. The base encoded stream may comprise the output of a base encoder of the base codec 320. A first encoded stream (an encoded level 1 stream) is created by reconstructing the encoded base stream to create a base reconstruction, and then taking the difference between the base reconstruction and the down-sampled version of the input video 30. Reconstructing the encoded base stream may comprise receiving a decoded base stream from the base codec (i.e. the input to processing block 300-1 comprises a base decoded stream as shown in FIG. 1). The difference signal is then processed at block 300-1 to create the encoded level 1 stream. Block 300-1 comprises a transform block 310-1, a quantization block 320-1 and an entropy encoding block 330-1. A second encoded stream (an encoded level 2 stream) is created by up-sampling a corrected version of the base reconstruction, using the up-sampler 305U, and taking the difference between the corrected version of the base reconstruction and the input signal 30. This difference signal is then processed at block 300-2 to create the encoded level 2 stream. Block 300-2 comprises a transform block 310-2, a quantization block 320-2, an entropy encoding block 330-2 and a residual processing block 350-2. As per processing block 300-1, the blocks may be performed in the order shown in the Figures (e.g. residual processing followed by transformation followed by quantization followed by entropy encoding).

A quantization scheme may be useful to create the residual signals into quanta, so that certain variables can assume only certain discrete magnitudes. In one case quantizing comprises actioning a division by a pre-determined step-width. This may be applied at both levels (1 and 2). For example, quantizing at block 320 may comprise dividing transformed residual values by a step-width (e.g. where an integer quotient is used to generate the quantized value and a remainder is ignored). The step-width may be pre-determined, e.g. selected based on a desired level of quantization. In one case, division by a step-width may be converted to a multiplication by an inverse step-width, which may be more efficiently implemented in hardware. In this case, dequantizing, such as at block 320, may comprise multiplying by the step-width. Entropy encoding as described herein may comprise run length encoding (RLE), then processing the encoded output is processed using a Huffman encoder. In certain cases, only one of these schemes may be used when entropy encoding is desirable.

The encoded base stream may be referred to as the base level stream.

As described earlier, residuals are computed by comparing an original form of an image signal with a reconstructed form of an image signal. For example, in one case, residuals for an L-2 enhancement stream are determined by subtracting an output of the upsampling from an original form of an image signal (e.g. the input video as indicated in the Figures). The input to the upsampling may be said to be a reconstruction of a signal following a simulated decoding. In another case, residuals for an L-1 enhancement stream are determined by subtracting an image stream output by the base decoder from a downsampled form of the original image signal (e.g. the output of the downsampling).

A decoder 400 that performs a decoding process corresponding to the encoder of FIG. 3 is depicted in the block diagram of FIG. 4. The decoding process is split into two halves as shown by the dashed line. Below the dashed line is the base level of the decoder 400, which may usefully be implemented in hardware. Above the dashed line is the enhancement level, which may usefully be implemented in software. The decoder 400 may comprise only the enhancement level processes, or a combination of the base level processes and enhancement level processes as needed. The decoder 400 may usefully be implemented in software, especially at the enhancement level, and may suitably sit over legacy decoding technology, particularly legacy hardware technology. By legacy technology, it is meant older technology previously developed and sold which is already in the marketplace, and which would be inconvenient and/or expensive to replace, and which may still serve a purpose for decoding signals. In other cases, the base level may comprise any existing and/or future video encoding tool or technology.

The decoder topology at a general level is as follows. The decoder 400 comprises an input (not shown) for receiving one or more input signals comprising the encoded base stream, the encoded level 1 stream, and the encoded level 2 stream together with optional headers containing further decoding information. The decoder 400 comprises a base decoder 420 at the base level, and processing blocks 400-1 and 400-2 at the enhancement level. An up-sampler 405U is also provided between the processing blocks 400-1 and 400-2 to provide processing block 400-2 with an up-sampled version of a signal output by processing block 400-1. The base decoder 420 may correspond to the base decoder 210 of FIG. 2, the processing block 400-1 may correspond to the first decoder 211 of FIG. 2, the processing block 400-2 may correspond to the second decoder 214 of FIG. 2 and the upsampler 405U may correspond to the upsampler 213 of FIG. 2.

The decoder 400 receives the one or more input signals and directs the three streams generated by the encoder 300. The encoded base stream is directed to and decoded by the base decoder 420, which corresponds to the base codec 420 used in the encoder 300, and which acts to reverse the encoding process at the base level. The encoded level 1 stream is processed by block 400-1 of decoder 400 to recreate the first set of residuals created by encoder 300. Block 400-1 corresponds to the processing block 300-1 in encoder 300, and at a basic level acts to reverse or substantially reverse the processing of block 300-1. The output of the base decoder 420 is combined with the first set of residuals obtained from the encoded level 1 stream. The combined signal is up-sampled by up-sampler 405U. The encoded level 2 stream is processed by block 400-2 to recreate the further residuals created by the encoder 300. Block 400-2 corresponds to the processing block 300-2 of the encoder 300, and at a basic level acts to reverse or substantially reverse the processing of block 300-2. The up-sampled signal from up-sampler 405U is combined with the further residuals obtained from the encoded level 2 stream to create a level 2 reconstruction of the input signal 30. The output of the processing block 400-2 may be seen as decoded video similar to the decoded video 250 of FIG. 2.

As noted above, the enhancement stream may comprise two streams, namely the encoded level 1 stream (a first level of enhancement) and the encoded level 2 stream (a second level of enhancement). The encoded level 1 stream provides a set of correction data which can be combined with a decoded version of the base stream to generate a corrected picture.

FIG. 5 shows the encoder 300 of FIG. 1 in more detail. The encoded base stream is created directly by the base encoder 320E, and may be quantized and entropy encoded as necessary. In certain cases, these latter processes may be performed as part of the encoding by the base encoder 320E. To generate the encoded level 1 stream, the encoded base stream is decoded at the encoder 300 (i.e. a decoding operation is applied at base decoding block 320D to the encoded base stream). The base decoding block 320D is shown as part of the base level of the encoder 300 and is shown separate from the corresponding base encoding block 320E. For example, the base decoder 320D may be a decoding component that complements an encoding component in the form of the base encoder 320E with a base codec. In other examples, the base decoding block 320D may instead be part of the enhancement level and in particular may be part of processing block 300-1.

Returning to FIG. 5, a difference between the decoded base stream output from the base decoding block 320D and the down-sampled input video is created (i.e. a subtraction operation 310-S is applied to the down-sampled input video and the decoded base stream to generate a first set of residuals). Here the term residuals is used in the same manner as that known in the art, that is, residuals represent the error or differences between a reference signal or frame and a desired signal or frame. Here the reference signal or frame is the decoded base stream and the desired signal or frame is the down-sampled input video. Thus the residuals used in the first enhancement level can be considered as a correction signal as they are able to ‘correct’ a future decoded base stream to be the or a closer approximation of the down-sampled input video that was used in the base encoding operation. This is useful as this can correct for quirks or other peculiarities of the base codec. These include, amongst others, motion compensation algorithms applied by the base codec, quantization and entropy encoding applied by the base codec, and block adjustments applied by the base codec.

The components of block 300-1 in FIG. 3 are shown in more detail in FIG. 5. In particular, the first set of residuals are transformed, quantized and entropy encoded to produce the encoded level 1 stream. In FIG. 5, a transform operation 310-1 is applied to the first set of residuals; a quantization operation 320-1 is applied to the transformed set of residuals to generate a set of quantized residuals; and, an entropy encoding operation 330-1 is applied to the quantized set of residuals to generate the encoded level 1 stream at the first level of enhancement. However, it should be noted that in other examples only the quantization step 320-1 may be performed, or only the transform step 310-1. Entropy encoding may not be used, or may optionally be used in addition to one or both of the transform step 110-1 and quantization step 320-1. The entropy encoding operation can be any suitable type of entropy encoding, such as a Huffman encoding operation or a run-length encoding (RLE) operation, or a combination of both a Huffman encoding operation and a RLE operation.

The selection of entropy coding schemes such as these may, in combination with the described quantization, have an advantageous effect upon the coding performance. This may be understood in view of the tendency for the application of a high degree of quantization to residual data as described in this disclosure to result in a high proportion of zero values. Run length encoding as mentioned above is particularly suited to the encoding of data having such a distribution, and thus these approaches can synergistically improve the efficiency of the overall process. Likewise, for embodiments wherein quantization is applied with greater step-width values, and the distribution of quantized data is such that a plurality of integer values are present in relatively greater numbers, the efficiency of the encoding process would typically benefit from the use of prefix/Huffman encoding, which is particularly suited to such distributions. This is especially the case given the distributions of residuals wherein higher integer values have lower frequencies. These forms of distributions may be efficiently encoded with Huffman encoding, which works by allotting fewer bits to high-frequency symbols. In this way, the quantization and the entropy encoding operation are complementary.

As noted above, the enhancement stream may comprise the encoded level 1 stream (the first level of enhancement) and the encoded level 2 stream (the second level of enhancement). The first level of enhancement may be considered to enable a corrected video at a base level, that is, for example to correct for encoder and/or decoder artefacts. The second level of enhancement may be considered to be a further level of enhancement that is usable to convert the corrected video to the original input video or a close approximation thereto (e.g. to add detail or sharpness). For example, the second level of enhancement may add fine detail that is lost during the downsampling and/or help correct from errors that are introduced by one or more of the transform operation 310-1 and the quantization operation 320-1.

Referring to FIG. 3 and FIG. 5, to generate the encoded level 2 stream, a further level of enhancement information is created by producing and encoding a further set of residuals at block 300-2. The further set of residuals are the difference between an up-sampled version (via up-sampler 305U) of a corrected version of the decoded base stream (the reference signal or frame), and the input signal 30 (the desired signal or frame).

To achieve a reconstruction of the corrected version of the decoded base stream as would be generated at the decoder 400, at least some of the processing steps of block 300-1 are reversed to mimic the processes of the decoder 200, and to account for at least some losses and quirks of the transform and quantization processes. To this end, block 300-1 comprises an inverse quantize block 320-1 i and an inverse transform block 310-1 i. The quantized first set of residuals are inversely quantized at inverse quantize block 320-1 i and are inversely transformed at inverse transform block 310-1 i in the encoder 100 to regenerate a decoder-side version of the first set of residuals.

The decoded base stream from decoder 320D is combined with this improved decoder-side version of the first set of residuals (i.e. a summing operation 310-C is performed on the decoded base stream and the decoder-side version of the first set of residuals). Summing operation 310-C generates a reconstruction of the down-sampled version of the input video as would be generated in all likelihood at the decoder—i.e. a reconstructed base codec video). As illustrated in FIG. 3 and FIG. 5, the reconstructed base codec video is then up-sampled by up-sampler 305U.

The up-sampled signal (i.e. reference signal or frame) is then compared to the input signal 30 (i.e. desired signal or frame) to create a second set of residuals (i.e. a difference operation 300-S is applied to the up-sampled re-created stream to generate a further set of residuals). The second set of residuals are then processed at block 300-2 to become the encoded level 2 stream (i.e. an encoding operation is then applied to the further or second set of residuals to generate the encoded further or second enhancement stream).

In particular, the second set of residuals are transformed (i.e. a transform operation 310-2 is performed on the further set of residuals to generate a further transformed set of residuals). The transformed residuals are then quantized and entropy encoded in the manner described above in relation to the first set of residuals (i.e. a quantization operation 320-2 is applied to the transformed set of residuals to generate a further set of quantized residuals; and, an entropy encoding operation 320-2 is applied to the quantized further set of residuals to generate the encoded level 2 stream containing the further level of enhancement information). However, only the quantization step 20-1 may be performed, or only the transform and quantization step. Entropy encoding may optionally be used in addition. Preferably, the entropy encoding operation may be a Huffman encoding operation or a run-length encoding (RLE) operation, or both.

Thus, as illustrated in FIGS. 3 and 5 and described above, the output of the encoding process is a base stream at a base level, and one or more enhancement streams at an enhancement level which preferably comprises a first level of enhancement and a further level of enhancement. As discussed with reference to previous examples, the operations of FIG. 5 may be applied in parallel to coding units or blocks of a colour component of a frame as there are no inter-block dependencies. The encoding of each colour component within a set of colour components may also be performed in parallel (e.g. such that the operations of FIG. 5 are duplicated according to (number of frames)*(number of colour components)*(number of coding units per frame)). It should also be noted that different colour components may have a different number of coding units per frame, e.g. a luma (e.g. Y) component may be processed at a higher resolution than a set of chroma (e.g. U or V) components as human vision may detect lightness changes more than colour changes.

The encoded base stream and one or more enhancement streams are received at the decoder 400. FIG. 6 shows the decoder of FIG. 4 in more detail.

The encoded base stream is decoded at base decoder 420 in order to produce a base reconstruction of the input signal 30 received at encoder 300. This base reconstruction may be used in practice to provide a viewable rendition of the signal 30 at the lower quality level. However, the primary purpose of this base reconstruction signal is to provide a base for a higher quality rendition of the input signal 30. To this end, the decoded base stream is provided to processing block 400-1. Processing block 400-1 also receives encoded level 1 stream and reverses any encoding, quantization and transforming that has been applied by the encoder 300. Block 400-1 comprises an entropy decoding process 430-1, an inverse quantization process 420-1, and an inverse transform process 410-1. Optionally, only one or more of these steps may be performed depending on the operations carried out at corresponding block 300-1 at the encoder. By performing these corresponding steps, a decoded level 1 stream comprising the first set of residuals is made available at the decoder 400. The first set of residuals is combined with the decoded base stream from base decoder 420 (i.e. a summing operation 410-C is performed on a decoded base stream and the decoded first set of residuals to generate a reconstruction of the down-sampled version of the input video—i.e. the reconstructed base codec video). As illustrated in FIG. 4 and FIG. 6, the reconstructed base codec video is then up-sampled by up-sampler 405U.

Additionally, and optionally in parallel, the encoded level 2 stream is processed at block 400-2 of FIG. 2 in order to produce a decoded further set of residuals. Similar to processing block 300-2, processing block 400-2 comprises an entropy decoding process 430-2, an inverse quantization process 420-2 and an inverse transform process 410-2. Of course, these operations will correspond to those performed at block 300-2 in encoder 300, and one or more of these steps may be omitted as necessary. Block 400-2 produces a decoded level 2 stream comprising the further set of residuals and these are summed at operation 400-C with the output from the up-sampler 405U in order to create a level 2 reconstruction of the input signal 30. The level 2 reconstruction may be viewed as an output decoded video such as 250 in FIG. 2. In certain examples, it may also be possible to obtain and view the reconstructed video that is passed to the upsampler 405U—this will have a first level of enhancement but may be at a lower resolution than the level 2 reconstruction.

Thus, as illustrated and described above, the output of the decoding process is an (optional) base reconstruction, and an original signal reconstruction at a higher level. This example is particularly well-suited to creating encoded and decoded video at different frame resolutions. For example, the input signal 30 may be an HD video signal comprising frames at 1920×1080 resolution. In certain cases, the base reconstruction and the level 2 reconstruction may both be used by a display device. For example, in cases of network traffic, the level 2 stream may be disrupted more than the level 1 and base streams (as it may contain up to 4× the amount of data where downsampling reduces the dimensionality in each direction by 2). In this case, when traffic occurs the display device may revert to displaying the base reconstruction while the level 2 stream is disrupted (e.g. while a level 2 reconstruction is unavailable), and then return to displaying the level 2 reconstruction when network conditions improve. A similar approach may be applied when a decoding device suffers from resource constraints, e.g. a set-top box performing a systems update may have an operation base decoder 220 to output the base reconstruction but may not have processing capacity to compute the level 2 reconstruction.

The encoding arrangement also enables video distributors to distribute video to a set of heterogeneous devices; those with just a base decoder 220 view the base reconstruction, whereas those with the enhancement level may view a higher-quality level 2 reconstruction. In comparative cases, two full video streams at separate resolutions were required to service both sets of devices. As the level 2 and level 1 enhancement streams encode residual data, the level 2 and level 1 enhancement streams may be more efficiently encoded, e.g. distributions of residual data typically have much of their mass around 0 (i.e. where there is no difference) and typically take on a small range of values about 0. This may be particularly the case following quantization. In contrast, full video streams at different resolutions will have different distributions with a non-zero mean or median that require a higher bit rate for transmission to the decoder.

In certain examples, residuals may be considered to be errors or differences at a particular level of quality or resolution. In described examples, there are two levels of quality or resolutions and thus two sets of residuals (L-1 and L-2). Each set of residuals described herein models a different form of error or difference. The L-1 residuals, for example, typically correct for the characteristics of the base encoder, e.g. correct artefacts that are introduced by the base encoder as part of the encoding process. In contrast, the L-2 residuals, for example, typically correct complex effects introduced by the shifting in the levels of quality and differences introduced by the L-1 correction (e.g. artefacts generated over a wider spatial scale, such as areas of 4 or 16 pixels, by the L-1 encoding pipeline). This means it is not obvious that operations performed on one set of residuals will necessarily provide the same effect for another set of residuals, e.g. each set of residuals may have different statistical patterns and sets of correlations.

In the examples described herein residuals are encoded by an encoding pipeline. This may include transformation, quantization and entropy encoding operations. It may also include residual ranking, weighting and filtering. These pipelines are shown in FIGS. 1 and 3A and 3B. Residuals are then transmitted to a decoder, e.g. as L-1 and L-2 enhancement streams, which may be combined with a base stream as a hybrid stream (or transmitted separately). In one case, a bit rate is set for a hybrid data stream that comprises the base stream and both enhancements streams, and then different adaptive bit rates are applied to the individual streams based on the data being processed to meet the set bit rate (e.g. high-quality video that is perceived with low levels of artefacts may be constructed by adaptively assigning a bit rate to different individual streams, even at a frame by frame level, such that constrained data may be used by the most perceptually influential individual streams, which may change as the image data changes).

The sets of residuals as described herein may be seen as sparse data, e.g. in many cases there is no difference for a given pixel or area and the resultant residual value is zero. When looking at the distribution of residuals much of the probability mass is allocated to small residual values located near zero—e.g. for certain videos values of −2, −1, 0, 1, 2 etc. occur the most frequently. In certain cases, the distribution of residual values is symmetric or near symmetric about 0. In certain test video cases, the distribution of residual values was found to take a shape similar to logarithmic or exponential distributions (e.g. symmetrically or near symmetrically) about 0. The exact distribution of residual values may depend on the content of the input video stream.

Residuals may be treated as a two-dimensional image in themselves, e.g. a delta image of differences. Seen in this manner the sparsity of the data may be seen to relate features like “dots”, small “lines”, “edges”, “corners”, etc. that are visible in the residual images. It has been found that these features are typically not fully correlated (e.g. in space and/or in time). They have characteristics that differ from the characteristics of the image data they are derived from (e.g. pixel characteristics of the original video signal).

As the characteristics of residuals differ from the characteristics of the image data they are derived from it is generally not possible to apply standard encoding approaches, e.g. such as those found in traditional Moving Picture Experts Group (MPEG) encoding and decoding standards. For example, many comparative schemes use large transforms (e.g. transforms of large areas of pixels in a normal video frame). Due to the characteristics of residuals, e.g. as described above, it would be very inefficient to use these comparative large transforms on residual images. For example, it would be very hard to encode a small dot in a residual image using a large block designed for an area of a normal image.

Certain examples described herein address these issues by instead using small and simple transform kernels (e.g. 2×2 or 4×4 kernels—the Directional Decomposition and the Directional Decomposition Squared—as presented herein). The transform described herein may be applied using a Hadamard matrix (e.g. a 4×4 matrix for a flattened 2×2 coding block or a 16×16 matrix for a flattened 4×4 coding block). This moves in a different direction from comparative video encoding approaches. Applying these new approaches to blocks of residuals generates compression efficiency. For example, certain transforms generate uncorrelated coefficients (e.g. in space) that may be efficiently compressed. While correlations between coefficients may be exploited, e.g. for lines in residual images, these can lead to encoding complexity, which is difficult to implement on legacy and low-resource devices, and often generates other complex artefacts that need to be corrected. Pre-processing residuals by setting certain residual values to 0 (i.e. not forwarding these for processing) may provide a controllable and flexible way to manage bitrates and stream bandwidths, as well as resource use.

For completeness, FIG. 7 illustrates a broad principle of the concept described herein in the form of a flowchart. The method 1000 includes: receiving an input video (step 1001); downsampling the input video to create a downsampled video (step 1002); instructing an encoding of the downsampled video using a base encoder to create a base encoded stream (step 1003); instructing a decoding of the base encoded stream using a base decoder to generate a reconstructed video (step 1004); comparing the reconstructed video to the downsampled video to create a first set of residuals (step 1005); and, encoding the first set of residuals to create a first level encoded stream, including: applying a transform to the first set of residuals to create a first set of coefficients (step 1006); applying a quantization operation to the first set of coefficients to create a first set of quantized coefficients (step 1007); and applying an encoding operation to the first set of quantized coefficients (step 1008), wherein applying the quantization operation comprises: adapting the quantization based on the first set of coefficients to be quantized, including varying a step-width used for different ones of the first set of coefficients, wherein a first set of parameters derived from the adapting is signalled to a decoder to enable dequantization of the first set of quantized coefficients.

FIG. 8A provides an example of how quantization of residuals and/or coefficients (transformed residuals) may be performed based on bins having a defined step width. The x-axis of FIG. 8A represents residual or coefficient values. In this example a number of bins are defined with a stepwidth of 5. The stepwidth may be understood as the quantization step size, as shown in the drawings. The size of the stepwidth may be selectable, e.g. based on a parameter value. In certain cases, the size of the stepwidth may be set dynamically, e.g. based on the rate control examples described above. In FIG. 8A, the stepwidth results in bins corresponding to residual values in the ranges of 0-4, 5-9, 10-14, 15-19 (i.e. 0 to 4 including both 0 and 4). Bin widths may be configured to include or exclude end points as required. In this example, quantization is performed by replacing all values that fall into the bin with an integer value (e.g. residual values of between 0 and 4 inclusive have a quantized value of 1). In FIG. 8A, quantization may be performed by dividing by the stepwidth (e.g. 5), taking the floor of the result (i.e. the nearest integer less than a decimal for positive values) and then adding one (e.g. 3/5=0.6, floor(0.6)=0, 0+1=1; or 16/5=3.2, floor(3.2)=3, 3+1=4). Negative values may be treated in a similar way, e.g. by working on absolute values then converting to negative values following calculation (e.g. abs(−9)=9, 9/5=1.8, floor(1.8)=1, 1+1=2, 2*−1=−2). FIG. 8A shows a case of linear quantization where all bins have a common stepwidth. It should be noted that various different implementations based on this approach may be enacted, for example, a first bin may have a quantized value of 0 instead of 1, or may comprise values from 1 to 5 inclusive. FIG. 8A is simply one illustration of quantization according to bins of a given stepwidth.

FIG. 8B shows how a so-called “deadzone” (DZ) may be implemented. This may be understood as a region around the zero output value of a quantizer, that is a band containing a zero signal and having a size that may be the same as or different from the stepwidth. Thus, for this band of inputs that are close to zero, the signal may effectively be attenuated so that low-level signals, which may typically correspond to noise in visual data, are not allocated data unnecessarily. In FIG. 8B, residuals or coefficients with a value within a pre-defined range are set to 0. In FIG. 8B the pre-defined range is a range around a value of 0. In FIG. 8B, values that are less than 6 and greater than −6 are set to 0. The deadzone may be set as a fixed range (e.g. −6 to 6) or may be set based on the stepwidth. In one case, the deadzone may be set as a predefined multiple of the stepwidth, e.g. as a linear function of a stepwidth value. In the example of FIG. 8B the deadzone is set as 2.4*stepwidth. Hence, with a stepwidth of 5, the deadzone extends from −6 to +6. In other case, the deadzone may be set as a non-linear function of a stepwidth value.

In one case, the deadzone is set based on a dynamic stepwidth, e.g. may be adaptive. In this case, the deadzone may change as the stepwidth changes. For example, if the stepwidth were updated to be 3 instead of 5, a deadzone of 2.4*stepwidth may change from a range of −6 to +6 to a range of −3.6 to 3.6; or, if the stepwidth is updated to be 10, the deadzone may change to extend from −12 to 12. In one case, the multiplier for the stepwidth may range from between 2 and 4. In one case, the multiplier may also be adaptive, e.g. based on operating conditions such as available bit rates.

Having a deadzone may help reduce an amount of data to be transmitted over a network, e.g. help reduce a bit rate. When using a deadzone, residual or coefficient values that fall into the deadzone are effectively ignored. This approach may also help remove low levels of residual noise. Having an adaptive, rather than constant, deadzone means that smaller residual or coefficient values are not overly filtered when the stepwidth decreases (e.g. if more bandwidth is available) and that a bit rate is suitably reduce if the stepwidth is increased. The deadzone need only be enacted at the encoder, the decoder simply receives a quantized value of 0 for any residual or coefficient that falls within the deadzone.

FIG. 8C shows how an approach called bin folding may be applied. In the example of FIG. 8C bin folding is used together with a deadzone, but in other cases it may be used without a deadzone and/or with other quantization approaches. In FIG. 8C, bin folding acts to place all residual or coefficient values that reside above a selected quantization bin into the selected bin. For example, this may be seen as a form of clipping.

In FIG. 8C, a stepwidth of 5 is again applied. A deadzone with a range of 2.4*stepwidth is also applied, such that values between −6 and 6 are set to 0. This can also be seen as following into a larger first quantization bin (having a value of 0). Two quantization bins with a width of 5 are then defined for positive and negative values. For example, a bin with a quantization value of 1 is defined between 6 and 11 (e.g. having a stepwidth of 5), and a bin with a quantization value of 2 is defined between 11 and 16. In this example, to enact bin folding, all residual or coefficients with a value that would normally fall into a bin above the second bin (e.g. that have a value greater than 16) are “folded” into the second bin, e.g. are clipped to have a quantization value of 2. This may be performed by setting all values greater than a threshold to the maximum bin value (e.g. 2). A similar process occurs for the negative values. This is illustrated in FIG. 8C by the large arrows.

Bin folding may be a selectable processing option at the encoder. It does not need to be enacted during dequantization at the decoder (e.g. “folded” or “clipped” values of 2 are simply dequantized as if they were in the second bin). Bin folding may be enacted to reduce a number of bits that are sent over a network to the decoder. Bin folding may be configurable so as to reduce a bit rate based on network conditions and/or base stream processing.

FIG. 8D shows how a quantization offset may be used in certain cases. A quantization offset may be used to shift locations of quantization bins. FIG. 8D shows a line indicating possible real world counts along the x-axis residual or coefficient value range. In this example, many values are near zero, with the count of higher values decreasing as you move away from 0. If a count value is normalised, the line may also indicate a probability distribution for residual or coefficient values.

The left-hand side bars, and the dashed lines on the right-hand side of FIG. 8D, illustrate a histogram that models quantization. For ease of explanation, count values for first to third bins following a deadzone are shown (for both positive and negative values, the latter being striped to illustrate the bars). For example, the bars show counts for quantized values of 1, 2, 3 and −1, −2, −3. Due to the quantization, the distribution modelled by the histogram differs from the actual distribution shown by the line. For example, an error—e—is shown that displays how the bar differs from the line.

To vary the properties of the error, e, a quantization offset—qO—may be applied. A quantization offset may be understood as a parameter the value of which defines whether or not and by what degree quantization intervals or bins are to be shifted from a predetermined or default location or set of values. For positive values, a positive quantization offset acts to shift each bin to the right and a negative quantization offset acts to shift each bin to the left. A quantization offset may be applied in combination with a deadzone. In one case, a deadzone may be applied based on a first set of thresholds, e.g. all values less than (n*stepwidth)/2 and greater than (n*stepwidth*−1)/2 are set to 0.

In some examples, the quantization offset may be signalled to the decoder for use in dequantization.

In one case, at the encoder, a quantization offset may be subtracted from a residual or coefficient value before quantization based on a stepwidth. Hence, in the decoder, a signalled offset may be added to a received quantized value for dequantization based on a stepwidth. In certain cases, the offset may be adjusted based on a sign of the residual or coefficient to allow for symmetrical operations about a 0 value. In one case, use of an offset may be disabled by setting a quantization or dequantization offset value to 0. In one case, an applied quantization offset may be adjusted based on a defined deadzone width. In one case, a deadzone width may be computed at the decoder, e.g. as a function of stepwidth and quantization parameters received from the encoder.

In one case, a stepwidth for quantization may be varied for different coefficients within a 2×2 or 4×4 block of coefficients. For example, a smaller stepwidth may be assigned to coefficients that are experimentally determined to more heavily influence perception of a decoded signal, e.g. in a 2×2 or 4×4 Directional Decomposition (DD-Squared or “DDS”) as described above A, H, V and D coefficients may be assigned smaller stepwidths with later coefficients being assigned larger stepwidths. In this case, a base_stepwidth parameter may be defined that sets a default stepwidth and then a modifier may be applied to this to compute a modified_stepwidth to use in quantization (and dequantization), e.g. modified_stepwidth=base_stepwidth*modifier (where “modifier” may be set based on a particular coefficient within a block or unit and may be derived from signalling such as the variable “qm” described below).

In certain cases, the modifier may also, or alternatively, be dependent on a level of enhancement. For example, a stepwidth may be smaller for the level 1 enhancement stream as it may influence multiple reconstructed pixels at a higher level of quality.

In certain cases, modifiers may be defined based on both a coefficient within a block and a level of enhancement. In one case, a quantization matrix may be defined with a set of modifiers for different coefficients and different levels of enhancement. This quantization matrix may be preset (e.g. at the encoder and/or decoder), signalled between the encoder and decoder, and/or constructed dynamically at the encoder and/or decoder. For example, in the latter case, the quantization matrix may be constructed at the encoder and/or decoder as a function of other stored and/or signalled parameters, e.g. those received via a configuration interface.

In one case, different quantization modes, or different schemes defining the quantization matrix that is to be applied to a given set of coefficients, may be defined. In one mode a common quantization matrix may be used for both levels of enhancement; in another mode, separate matrices may be used for different levels; in yet another mode, a quantization matrix may be used for only one level of enhancement, e.g. just for level 2. The quantization matrix may be indexed by a position of the coefficient within the block (e.g. 0 or 1 in the x direction and 0 or 1 in the y direction for a 2×2 block, or 0 to 3 for a 4×4 block).

In one case, a base quantization matrix may be defined with a set of values. This base quantization matrix may be modified by a scaling factor that is a function of a stepwidth for one or more of the enhancement levels. In one case, a scaling factor may be a clamped function of a stepwidth variable. At the decoder, the stepwidth variable may be received from the encoder for one or more of the level-2 stream and the level-1 stream.

By way of some further examples of processes that involve the above described principles, the advantageous modes of configuring and adapting the quantization in order to improve the encoding and decoding procedures further may be understood in further view of the following. Data block unit enhancement payload semantics may be applied, involving a number of parameters with which properties of the quantization and dequantization steps can be signalled and configured.

In an example, the parameter dequant_offset_signalled specifies whether or not the value of the offset parameter to be applied when dequantizing is signalled. In this way, it may be signalled whether or not the offset is sent. In this example, if the offset is sent, then it is used. If it is not sent, then a default offset, or no offset may be used.

In an example of a method of encoding an input video into a plurality of encoded streams, wherein the encoded streams may be combined to reconstruct the input video, the method may involve receiving an input video, and downsampling the input video to create a downsampled video.

The method typically further includes instructing an encoding of the downsampled video using a base encoder to create a base encoded stream, the base encoded stream; instructing a decoding of the base encoded stream using a base decoder to generate a reconstructed video; comparing the reconstructed video to the downsampled video to create a first set of residuals; and, encoding the first set of residuals to create a first level encoded stream. This preferably includes: applying a transform to the set of residuals to create a set of coefficients; applying a quantization operation to the set of coefficients to create a set of quantized coefficients; and applying an encoding operation to the quantized coefficients, wherein the quantization operation is performed using a quantization matrix, the quantization matrix being derived in accordance with an obtained value of a quantization matrix mode parameter.

As described earlier in this disclosure, a quantization matrix mode parameter can be advantageously used to specify the quantization matrix to be used in the encoding process. In some examples, when the quantization matrix mode parameter value is equal to a predetermined value, for example when it is equal to zero, 0, the methods may involve using a default quantization matrix for each of two levels of quality. These levels are typically, or typically correspond to, level 1 and level 2 enhancement streams. When the quantization matrix mode parameter value is equal to 1, a first quantization matrix may be used for each of the two levels of quality, and the first quantization matrix may be signalled, for example, from the encoder to a decoder or a device to which the encoded stream is to be transmitted. When the quantization matrix mode parameter value is equal to 2, a second quantization matrix can be used for level of quality 2, and the second quantization matrix may be signalled. In this case, no quantization matrix may be used for level of quality 1, or default values may be used for this level. When the quantization matrix mode parameter value is equal to 3, a third quantization matrix is preferably used for level of quality 1, or the first level encoded stream, and the third quantization matrix is signalled. In this case, no quantization matrix may be used for level of quality 2, or default values may be used for this level. When the quantization matrix mode parameter value is equal to 4, a fourth quantization matrix may be used for the first level encoded stream and a fifth quantization matrix may be used for the second level encoded stream (e.g. two matrices may be used), each of which can be equal or unequal to each other and any of the aforementioned first to third matrices. In this fifth mode, the fourth quantization matrix and the fifth quantization matrix may be signalled to a decoder or other device to which the encoded stream is to be transmitted.

In the procedures described in this disclosure, as alluded to above, every group of transform coefficients passed to this process typically belongs to a specific plane and layer. Typically, they have been scaled using a linear quantizer which in some examples uses a non-centred dequantization offset. A scaling process for the transform coefficients may be applied as follows. The process may take location information for a block, together with a set of parameters by which properties of the block, such as its size, the properties of the quantization operation, such as step width and offset value, and the level of enhancement to which it applies, may be indicated. For instance, a location, which may be indicated with a pair of coordinate values, or for example parameters (xTbP, yTbP) specifying the top-left sample of the current luma or chroma transform block relative to the top left luma or chroma sample of the current picture may be used. This can be related to a particular portion of data representative of an image, and may for instance be related either to a luma plane or a chroma plane, depending on the plane to which the transform coefficients belong.

The aforementioned parameter specifying the size of a current transform block, which may be called nTbS in some examples, may have a value that is dependent upon the type of transform, and in particular upon the value of a parameter that defines, the transform used for decoding. This type parameter may be called transform_type in some examples, and in some applications may have a value of 0, 1, or 2-3, respectively corresponding to a 2×2 directional decomposition transform, a 4×4 directional decomposition transform, or a value or parameter specifying that the values are zero as those elements are not used in the bitstream. In some examples this transform_type parameter having a value of 0, may correspond to a number of layers being equal to 4 and if transform_type is equal to 1 the corresponding number of layers value may be 16. The size, nTbS, parameter may have a value of 2 if the transform_type is equal to zero and may have a value of 4 if transform_type is equal to 1.

A further input to the process is typically in the form of an array of entropy decoded quantized coefficients. This may be called TransCoeffQ, and be of a size related to the size parameter noted above, in particular having dimensions (nTbS)×(nTbS), i.e. the array may comprise a coding unit or block as described herein. This array may be called TransCoeffQ. The stepwidth value may be specified with a parameter called stepWidth. The index of the level of enhancement may be specified by a parameter also, and in some examples may be called idxLevel. If a dequantization offset is to be used, this may be specified with a parameter, for example called dQuantOffset. The parameter typically specifies the value of the dequantization offset parameter to be applied. This process typically results in an array of dequantized transform coefficients. This may for example be in the form of an array of dimensions (nTbS)×(nTbS) containing elements d[x][y].

The values in the output array, which may be called d[x][y], are typically derived based upon the stepwidth, as described above for example, and/or any of the index of the level of enhancement, as applied to the quantization matrix, which may be referred to as qm, and the entropy decoded quantized coefficients, which may be named TransCoeffQ[xTbP][yTbP] for the corresponding element. Additionally, the values of the output array elements may be derived by way of additionally applying an offset to the result of the aforementioned operation. For example, a parameter, which may be named appliedOffset, may be added to the value to produce the output element value.

This may therefore involve the following relation to calculate each element d of the array:

d[x][y]=(TransCoeffQ[xTbP][yTbP]*(stepWidth*qm[yTbP+(idxLevel*4)][xTbP]))+appliedOffset

In the above calculation, which is one example of how dequantization may be performed at a decoder, idxLevel may be 0 or 1 representing levels 2 and 1 respectively. In the above calculation, and as described in other examples herein, a value from the quantization matrix is selected based on a particular coefficient and a particular level of enhancement.

With regard to the offset that may be applied in some examples, as shown in the relation above, this parameter, which may be called appliedOffset, is typically derived as follows below.

In certain cases, the appliedOffset may be conditionally applied based on whether or not residual filtering is to be applied. This filtering may be applied following the inverse transformation (e.g. 410-1 in FIG. 6) and may be a deblocking filter. In these cases, an offset may only be applied if residual filtering is applied. For example, if a parameter that specifies whether or not deblocking filter should be applied, which may be named deblocking signalled, has a particular value, for example is equal to 1, calculating the offset based on the aforementioned parameter specifying the dequantization offset, which may be named dQuantOffset.

The appliedOffset may also be derived from a single value of dQuantOffset and adapted for application to positive and negative coefficient values. For instance, appliedOffset may be configured as a negative or positive value, typically having the same absolute value as the dequantization offset parameter and having a positive or negative sign respectively. This may be applied if the TransCoeffQ value has a value less than zero; otherwise, if the TransCoeffQ value is greater than zero, the applied offset may be assigned a value equal to that of the dequantization offset parameter. If the TransCoeffQ value is equal to zero, then the applied offset may be assigned a zero value also.

Thus, in certain examples, the derivation of the offset that may be added to obtain the output array elements may be derived by (where TransCoeffQ may be an array, typically of size (nTbS)×(nTbS), containing entropy decoded quantized coefficients):

  If deblocking_signalled is equal to 1  If TransCoeffQ[ xTbP ][ yTbP ] < 0   appliedOffset = (dQuantOffset * −1)  else If TransCoeffQ [ xTbP ][ yTbP ] > 0   appliedOffset = dQuantOffset  else   appliedOffset = 0  else   appliedOffset = 0

By way of an example, a parameter or set of parameters may be signalled as described above and used to specify how the quantization matrix is to be applied to a set of coefficients. This parameter may be a quant_matrix_mode parameter, which specifies which quantization matrix to be used in the decoding process in accordance with Table 1 below. The parameter may be one byte. The quant_matrix_mode parameter may be used to configured how the quantization matrix is derived (e.g. as described later below).

TABLE 1  Quantization matrix quant_matrix_mode Value of type 0 Both levels of   quality use default matrices 1 One matrix of  modifiers is signalled and  should be used  on both  levels of quality 2 One matrix of  modifiers is signalled and  should be used  on level  of quality 2 3 One matrix of  modifiers is signalled and  should be used  on level  of quality 1 4 Two matrices  of modifiers  are signalled- the first one  for level of  quality 2, the  second for  level of  quality 1 5-7 Reserved_zeros

Quantization matrix data semantics may be applied according to the following specific example. A quantization matrix, which may be referred to as qm[y][x] is derived.

The matix qm may have dimensions k*M by N. That is to say, the matrix may be defined as corresponding to quantization coefficients contained in a M by N matrix, and qm may contain this data in the form of a respective M by N matrix of quantization coefficients for example, for each of k levels of enhancement.

In the present examples, two levels of enhancement are involved, namely levels 1 and 2 as described in this disclosure, and k is equal to 2. The largest transform as described in relation to these procedures is 4×4, and so M and N may each be equal to 4.

In some examples, by defining qm with M and N values that correspond to the largest available transform, which is a 4×4 transform in these examples as alluded to above, thus comprising 16 coefficients, the application of the quantization matrix where a smaller transform is employed may be performed by reading from qm, and applying, a subset of the coefficients in the respective M by N matrix.

In a specific example, if the value of the quantization matrix mode parameter is equal to zero, namely if quant_matrix_mode is equal to 0, then the following default quantization matrix is used in the quantization operation:

qm[y][x]={0.500 0.500 0.500 0.617}{0.862 0.610 1.064 0.781}{0.500 0.500 0.500 0.617}{3.125 1.851 1.851 1.316}{0.500 0.500 0.500 0.617}{0.862 0.610 1.064 0.781}{0.862 1.064 0.610 0.781}{3.125 1.851 1.851 1.316},

wherein y=0 . . . 3 are coefficients to be used for the level 2 of the enhancement and y=4 . . . 7 are coefficients to be used for the level 1 of the enhancement; and

if the value of the quantization matrix mode parameter is equal to 1, one matrix of modifiers is signalled is used on both levels of enhancement. A matrix may be built by way of an interative process in some applications. Thus qm[x][y] values may be obtained row-by-row, whereby for each row, with a value in that row and for each column of the matrix is populated as the row is processed. A stepwidth modifier parameter may be used in obtaining these matrix values, as described below. In the present example, the number of rows to be populated may be 8, with the number of columns being equal to 4, as described above in connection with the matrix dimensions in this specific example. In particular, the iterative process for this example may be written as:

  for (y = 0; y < 8; y++)  for (x = 0; x < 4; x++)   qm[y][ x ] = step_width_modifier_2[x + y*4],

wherein stepwidth modifier parameter, which may be called step_width_modifier_2 in this example, is a parameter specifying the values of level 2 enhancement 16 coefficients to be applied at the different layers of the transform coefficients; and

if the value of the quantization matrix mode parameter is equal to 2, one matrix of modifiers is signalled and is used on level of enhancement 2. Likewise an iterative procedure corresponding to that described above may be used, in particular:

  for (y = 0; y < 4; y++)  for (x = 0; x < 4; x < 4)   qm[ y][ x ] = step_width_modifier_2[x + y*4]; and

if the value of the quantization matrix mode parameter is equal to 3, one matrix of modifiers is signalled and is used on level of enhancement 1:

  for (y = 0; y < 4; y++)  for (x = 0; x < 4; x < 4)   qm[y + 4][ x ] = step_width_modifier_2[x + y*4]; and

if the value of the quantization matrix mode parameter is equal to 4, two matrixes of modifiers are signalled, the first one for level of enhancement 2, the second for level of enhancement 1:

  for (y = 0; y < 4; y++)  for (x = 0; x < 4; x < 4)   qm[ y][ x ] = step_width_modifier_2[x + y*4] for (y = 0; y < 4; y++)  for (x = 0; x < 4; x < 4)   qm[ y][ x ] = step_width_modifier_1[x + y*4],

wherein step_width_modifier_1 is a parameter specifying the values of level 1 enhancement 16 coefficients to be applied at the different layers of the transform coefficients.

As described above, the values of d[x][y] in the matrix may be calculated for example as the product of the respective transform coefficient at the relevant element in the matrix and a sum of the corresponding quantization matrix element value at a column identified by a product of a parameter specifying the size of a current transform block and a levelIdxSwap parameter, and in the corresponding row, and the stepwidth modifier parameter value corresponding to that element, with the above described offset typically being additively applied to that product also.

The above described dequantization process may be performed in accordance with the following ordered steps. The dequantization process as per the aforementioned examples may be invoked with the luma location (xTbY, yTbY), the transform size set equal to nTbS, namely the size of a current transform block, the array TransCoeffQ, as described above, of a size (nTbS)×(nTbS), and the step width parameter as inputs. The output may then be an array of dequantized coefficients (e.g. dequantized transformed residuals) of dimensions (nTbS)×(nTbS), which may be referred to a dequantCoeff in this example.

Further example methods in which asymmetric quantization is employed are now described. For simplicity, along the description the illustrated examples usually adopt the use case of video sequences, i.e., a time-based signal consisting of a sequence of 2D images (commonly called “frames”, or “fields” in the case of interlaced video signals), with each element (in such non-limiting example case typically referred to as “pixel”) being characterized by a set of colour settings in a suitable colour space (e.g., YUV, RGB, HSV, etc.). Different colour planes (e.g., the luminance-Y plane and the two chrominance—U and V—planes) are often encoded separately, and often with different resolutions (due to the lower sensitivity of human eyes to chrominance information).

In other cases the signal is represented as a sequence of N-dimensional samples, and the examples refer to the fact that the full representation of the signal is an (N+1)-dimensional signal (e.g., if one of the dimensions is time, this corresponds to representing a sequence of spatial renditions with a single time-space rendition). These are to be considered non-limiting examples of the possible kinds of signals that can be filtered and/or compressed using innovative methods described herein. For signals other than images or videos, people skilled in the art can easily apply methods described herein by suitably adapting the approaches described for the use case of images and video signal. In a non-limiting example, time samples of the signal can also be hyperplanes of elements with a different number of dimensions other than 2 (e.g., one-dimensional time samples, three-dimensional time samples, etc.) and/or it is possible to apply to dimensions different than time approaches that correspond to the ones described herein for the time dimension.

Every sample in time of a signal is represented with a hyperplane (or more simply “plane”, intended in its broadest meaning as “set of elements organized as an array with one or more dimensions”): for example a multichannel sound signal, a 2D HD video frame, or a 3D volumetric medical image can be all represented with arrays of plane elements (specifically, a 1D plane of elements for the multichannel sound signal, a 2D plane of elements for the HD video frame and a three-dimensional hyperplane of elements for the volumetric medical image).

Along time, samples occur at a given (local) sample rate. In conventional methods, the sample rate is always constant. On the contrary, innovative methods illustrated herein also allow for variable sample rate. In particular for levels of quality lower than the topmost level, the duration of time samples of a given level of quality (i.e., the sample rate for that given level of quality) can be variable, effectively representing variable sample rates. In order to allow for suitable signal playback with time samples that have different time durations, each of the time sample is also characterized by a “picture number” or “picture identifier”, representing the time when the time sample should start being displayed.

Methods and embodiments illustrated herein can be used in conjunction with one another and/or with other methods. Many of the preferred embodiments illustrated herein describe techniques and algorithms with the goal of achieving compression, i.e., encoding and/or decoding a suitable rendition of the signal with a minimum quantity of bits. This also is a non-limiting example: other embodiments can achieve different purposes, such as multiscale encoding and decoding, graceful degradation and recovery, adaptive streaming, robust and efficient filtering, signal denoising (e.g., image denoising, video denoising, etc.), signal enhancements (e.g., signal supersampling, de-interlacing, etc.), generation of signal quality metrics, content identification, machine vision, signal encryption (e.g., secure communication), etc.

As alluded to above, quantization at the encoder and dequantization at the decoder may be performed asymmetrically. That is, quantization or dequantization steps proposed in the present disclosure may be performed differently or according to different or modified parameters. Such solutions provide particular utility to vary detail in reconstructed data and to provide more detail in particular areas of a statistical distribution. In particular, such asymmetry provides more precise quantization for information responsible for detail in a reconstructed set of data (e.g. where the encoded data is residuals data which is combined with image data to reconstruct an image, residuals data responsible for detail at the edges may be dequantized more precisely).

Such asymmetric quantization may have particular utility in tier-based coding schemes such as those described earlier in this disclosure. In certain examples, a decoding process may be performed both at an encoder side in a reconstruction pathway and at the decoding side when reconstructing the encoded information. In further examples, parameters are signaled by the encoder process to the decoder process to instruct or facilitate asymmetric modification of the dequantize process. In a specific example, where an encoder quantizes with a certain stepwidth, the corresponding decoding may utilize this stepwidth and modify according to signaled parameters received by the decoder from the encoder.

As noted, quantization or dequantization steps proposed in the present disclosure may be performed differently or according to different or modified parameters. In particular, dequantization offset or stepwidth modifiers proposed above may be provided at a decoder only, such that offsets and/or modifiers are not applied during encoding. For example, a step-width may be used in encoding quantization and not modified according to examples provided in the above disclosure.

The following provides an explanation and a set of non-limiting examples of asymmetric quantization.

In an example, asymmetric means that the encoder quantizes with a Stepwidth (SWenc) and the decoder dequantizes with a slightly different stepwidth (SWenc+SWmodifier). SWmodifier is not fixed, but adaptively depends on SWenc according to a non-linear function, based at least in part on a parameter that can be signalled (or, alternatively based in part on the default value of said parameter). Examples of stepwidth modifiers are set out in the appended figures.

In a specific implementation of this example, each directional stepwidth (calculated from the master stepwidth according to the adaptive quantization matrix logic) gets a different modifier, and the formula can be modulated by an optional signalled parameter. Asymmetric dequantization allows to statistically “undershoot” with the dequantization of coefficients in the buckets closer to the center (zero) of the laplacian distribution, while the more you go towards the tail buckets, the more dequantization happens towards the center or even the tail-most end of the bucket. That is, low risk of mosquito noise for the transition between deadzone and +/−1 buckets, and more precise dequantization for the residuals responsible for sharper/higher-contrast edges.

The possibility of the encoder to “regulate” the knob of the adaptive SWmodifier allows the encoder to more precisely reconstruct the residuals that matter. An encoder according to principles proposed herein also has the possibility to signal another example mode. This this mode the dequantization stepwidth is symmetric with encoding, and uses a dequantization offset signalled by the encoder. Also in this case, however, things are not that simple: the dequantization offset may be set with reference to the master stepwidth, and may be modulated adaptively based on each direction stepwidth derived from the master stepwidth. That is, each stepwidth in the quantization matrix may get a different offset, and the way the modulation works follows an assumption of laplacian distribution (i.e., exponential descent of the residual distribution). Example encoders according to principles proposed herein have many ways to play with this feature to their advantage. For example the encoder may ignore this feature and use default a quant matrix and a default dequant offset/SWmodifier derivation or an encoder may be a bit smarter, and during encoding try different signalled dequantization logics, ultimately choosing the one that produces the best metric (e.g., MSE, the basis for PSNR). This is because quantization can stay the same, and the encoder can easily try to dequantize according to different alternatives. A simple binary chop can quickly converge into the best Mode and/or the best signalled parameter for that frame. (i.e., simple Distortion Optimization at same rate, DO).

Such approaches can be optionally in certain non-limiting examples be made even more sophisticated if the encoder also starts playing with a quant matrix. In that case, the encoder can signal to a decoding process the quant matrix based on statistics of the residuals. And then also do the optimization mentioned above in order to further improve the accuracy of reconstruction. A further contemplated alternative is that, instead of signalling different dequantization parameters, the encoder can modulate the quantization stepwidth in order to be optimal for a given default dequantization method. That is, in this example, the encoder may encode a few times with slightly different stepwidths and choose the one that optimizes the trade-off between rate and distortion. In certain implementations it is proposed that the combination of custom quant matrix and custom dequantization parameters can be worth ca. 5-10% or more of enhancement compression vs. good average defaults.

It is further envisaged that, after choosing the best average dequantization parameters to signal, the encoder may want to change the value of some “unlucky” coefficients that are far from the dequantization point of their bucket. For instance, if an example encoder were to quantize with Stepwidth=1,000, DeadZone=2.4×Stepwidth (i.e., deadZoneOffset=0.2×Stepwidth) and on average for the frame a good dequantization point for bucket 10 is 10% (i.e., 10×stepwidth+deadZoneOffset+10%×stepwidth) and one coefficient is at 98% of that bucket, the encoder may want to decide to nudge that coefficient to bucket 11, so that it will be dequantized closer to the original value.

A further proposed non-limiting example process may make the dead zone adaptive with the coefficient Stepwidth, i.e., the width of the deadzone (bucket 0) and depend on a Stepwidth based on a formula. Interestingly, asymmetric dequantization intertwines with the deadzone, because the deadzone becomes dependent on (Stepwidth+SWmodifier).

Examples of related processes may be summarised by the following:

-   -   Asymmetric dequantization means that the dequantization point         may be different for each bucket, with centre-most buckets         having “conservative” dequantization points and tail-most         buckets having dequantization points progressively further from         the center     -   Dequantization of coefficients with larger Stepwidth happens         differently from dequantization of coefficients with smaller         Stepwidth. i.e., the quantization matrix also has an impact on         dequantization modifiers     -   The encoder can signal how to dequantize (Mode 0 or 1) as well         as influence the adaptive formulas     -   It is all closely linked with quantization matrix     -   Knowing the dequantization method that will be adopted by the         decoder, the encoder can further optimize the reconstruction of         some coefficients by nudging them into a neighbouring bucket         with dequantization point closer to the original value of the         coefficient.

Example Implementation

According to a particular example, scaled transformed coefficients may be given as d[x][y] and may be determined according to the following:

d[x][y]=(TransformCoeffQ[x][y]*((qm[x+(levelIdxSwap*nTbS)][y]+stepWidthModifier[x][y])+appliedOffset[x][y])

where:

TransformCoeffQ[x][y] represents an array of coefficients;

qm[x][y] represents a quantization matrix of quantization parameters or step widths;

stepWidthModifier[x][y] represents an array of modification parameters; and,

appliedOffset[x][y] is an array which functions as a dead zone, described elsewhere herein, that is, in certain signalled scenarios, a dequantisation offset may be used to change a dead zone or may be 0.

If a flag signals that a dequantization offset is not used, then the stepwidth modifier may be a function of the step width, e.g:

stepWidthModifier[x][y]=((((Floor(−Cconst*Ln(qm[x+(levelIdxSwap*nTbS)][y])))+Dconst)*(qm[x+(levelIdxSwap*nTbS)][y]²)))/32768)>>16

If a flag signals that a dequantization offset is used, then the step width modifier may be based on a set of offset values, e.g:

stepWidthModifier[x][y]=(Floor((dQuantOffsetActual[x][y])*(qm[x+(levelIdxSwap*nTbS)][y]))/32768)

where the offset values are a function of the step widths (i.e. qm[x][y]), e.g:

dQuantOffsetActual[x][y]=((Floor(−Cconst*Ln(qm[x+(levelIdxSwap*nTbs)][y])+(dQuantOffset<<9)+Floor(Cconst*Ln(StepWidth))))*(qm[x+(levelIdxSwap*nTbs)][y]))>>16

Dequantization Offsets

Methods and embodiments illustrated herein also include ways to suitably leverage predictably statistical characteristics of residual data in tier-based encoding methods. In particular, in the context of a tier-based hierarchy, residual data is distributed by construction according to known probability distribution patterns (e.g., by means of non-limiting example, laplacian distributions, poisson distributions, etc.): it is thus possible to rapidly identify key parameters said probability distributions and indicate them to a decoder by means of a relatively limited set of meta data.

At the same time, non-limiting embodiments herein also include ways to suitably adapt quantization and dequantization operations based on the known probability distribution of the original symbols, so as to improve at the same time compression efficiency, reconstruction quality and processing speed. Non-limiting embodiments herein also include methods to minimize signaling overhead by deriving with default formulas how the optimal dequantization offsets should be varied for each coefficient group based on the relative magnitude of their quantization step width.

Embodiments herein deviate with respect to conventional systems and methods, providing novel methods to dynamically adapt quantization and dequantization operations, based on suitable and concise indication of characteristics of the probability distribution of the original unquantized values.

According to a broad aspect, there is disclosed a method for rapidly assessing the probability distribution of original unquantized values, generating suitable signaling meta data based on said distribution and adapting quantization and dequantization operations based on said meta data. The disclosure also includes decoder-side methods to improve the statistical properties of dequantization offsets also in absence of signaling information from the encoder.

The concepts are especially valuable and effective in the context of tier-based encoding methods, since a tier-based hierarchy is produced in a way that implicitly defines the type of probability distribution of residual values, which in turn allows both to characterize each specific probability distribution with relatively few and quickly identifiable parameters and to assume a number of properties of dequantization offsets (such as for instance their relationship with the magnitude of quantization step widths). According to non-limiting embodiments described herein, the novel dequantization methods described herein are used in-loop within the encoding and decoding hierarchy of a tier-based encoding method.

For simplicity, non-limiting embodiments illustrated herein refer to a signal as a sequence of samples (i.e., two-dimensional images, video frames, video fields, sound frames, etc.). In the description the terms “image”, “picture” or “plane” (intended with the broadest meaning of “hyperplane”, i.e., array of elements with any number of dimensions and a given sampling grid) will be often used to identify the digital rendition of a sample of the signal along the sequence of samples, wherein each plane has a given resolution for each of its dimensions (e.g., X and Y), and comprises a set of plane elements (or “element”, or “pel”, or display element for two-dimensional images often called “pixel”, for volumetric images often called “voxel”, etc.) characterized by one or more “values” or “settings” (e.g., by ways of non-limiting examples, color settings in a suitable color space, settings indicating density levels, settings indicating temperature levels, settings indicating audio pitch, settings indicating amplitude, etc.). Each plane element is identified by a suitable set of coordinates, indicating the integer positions of said element in the sampling grid of the image. Signal dimensions can include only spatial dimensions (e.g., in the case of an image) or also a time dimension (e.g., in the case of a signal evolving over time).

As non-limiting examples, a signal can be an image, an audio signal, a multichannel audio signal, a telemetry signal, a video signal, a multi-view video signal (e.g., 3D video), a volumetric signal (e.g., medical imaging, scientific imaging, holographic imaging, etc.), a volumetric video signal, or even signals with more than four dimensions.

For simplicity, non-limiting embodiments illustrated herein often refer to signals that are displayed as 2D planes of settings (e.g., 2D images in a suitable color space), such as for instance a video signal. The terms “frame” or “field” will be used interchangeably with the term “image”, so as to indicate a sample in time of the video signal: any concepts and methods illustrated for video signals made of frames (progressive video signals) can be easily applicable also to video signals made of fields (interlaced video signals), and vice versa. Despite the focus of embodiments illustrated herein on image and video signals, people skilled in the art can easily understand that the same concepts and methods are also applicable to any other types of multidimensional signal (e.g., audio signals, volumetric signals, stereoscopic video signals, 3DoF/6DoF video signals, plenoptic signals, etc.).

According to non-limiting embodiments described herein, a signal processor (e.g., computer processor hardware) is configured to receive data to quantize and a corresponding reference quantization step width to be used to quantize it. Based at least in part on the quantization step width, the signal processor identifies the range of unquantized values corresponding to quantized value zero (“Dead Zone”). In some non-limiting embodiments, the encoder calculates the Dead Zone from the reference step width based on a formula that is known to both encoder and decoder, which allows to vary the relative size of the Dead Zone quantization bin without requiring any signalling overhead. In a non-limiting embodiment, Dead Zone is calculated as a multiple of quantization step width (i.e., Dead Zone=DZmultiplier*StepWidth), wherein the DeadZone multiplier DZmultiplier is calculated as a linear function of StepWidth (i.e., DZmultiplier=a*StepWidth+b, wherein a and b are suitable constants known to both encoder and decoder). In other non-limiting embodiments, the relationship between DZmultiplier is non-linear. In some non-limiting embodiments, DZmultiplier is represented with a fixed-point integer number, so as to avoid using hardware-specific floating point computations.

According to non-limiting embodiments described herein, the encoder, based at least in part on a metric calculated on data to quantize, identifies a dequantization offset value δ to add to the center-most extreme of the each range of unquantized values (“Quantization Bin”) in order to obtain the dequantized value {circumflex over (R)}_(ι) corresponding to Q_(i). Dequantization of a quantized symbol Q is performed by both encoder (for simulated decoding) and decoder as follows.

If the quantized symbol Q_(i) is positive:

R _(i) =Q _(i)·(StepWidth·QuantizationMatrix)+(δ−DeadZone)

If instead the symbol Q_(i) is negative:

R _(i) =Q _(i)·(StepWidth·QuantizationMatrix)−(δ−DeadZone)

In some non-limiting embodiments, the decoder produces dequantization offset value δ by analyzing the signal encoded bytestream. If a signaling bit corresponding to dequantization offset is set to 1, the decoder processes the following byte in the bytestream so as to produce a reference dequantization offset.

According to third non-limiting embodiments, for a plurality of sets of encoded data (e.g., groups of encoded transformed coefficients) the encoder signals to the decoder a single parameter corresponding to a reference quantization StepWidth, and the decoder leverages a quantization matrix to process the parameter corresponding to the reference StepWidth and derive a distinct quantization StepWidth for each of the groups of transformed coefficients. In order to limit the number of dequantization offsets to be transmitted as metadata in the bytestream, both encoder and decoder automatically derive from a single reference dequantization offset value δ—intended as the offset corresponding to the signaled reference StepWidth for the data layer—to a specific dequantization offset value δ_(i) for each of the encoded coefficient groups, said offset value δ_(i) being dependent at least in part on signaled dequantization offset δ and on quantization StepWidth_(i) of set of coefficients i.

In a non-limiting embodiment, the encoder and the decoder calculate offsets for all quantization StepWidths derived from the reference StepWidth by means of assuming a relationship as follows:

Dequantization_Offset_(k) =f(StepWidth_(k))

And consequently:

Actual_Dequantization_Offset_(i)=Reference_Dequantization_Offset+f(StepWidth_(i))−f(Reference_StepWidth)

In some non-limiting embodiments, f(StepWidth) is a logarithmic relationship.

In other non-limiting embodiments, the encoder derives a value for reference dequantization offset value δ by means of an RDO (“Rate Distorsion Optimization”) algorithm. By way of non-limiting example, in an embodiment the encoder calculates the reference dequantization offset value δ by performing a binary search optimization algorithm on the data to quantize, using a suitable metric (e.g., by way of non-limiting example, MSE, MAE, PSNR) to determine the fidelity of the rendition produced by dequantizing with a given dequantization offset value δ. In a non-limiting embodiment, the signal processor selects a value that minimizes the mean square error between original unquantized values and dequantized value {circumflex over (R)}_(ι). In other non-limiting embodiments, the encoder performs said optimization taking into account only residuals that correspond to areas of the signal that have been identified of relatively high priority, according to a suitable priority map.

In other non-limiting embodiments, the encoder selects a dequantization offset and detects coefficients values that are likely to be quantized far from the dequantization point of the corresponding quantization bucket. Accordingly, the encoder assigns to said coefficients the quantized symbol of a neighbouring quantization bucket, so as to reduce a metric of difference between the unquantized value of the coefficient and the dequantized value of the quantized symbol assigned to the coefficient. In a non-limiting embodiment, the encoder detects at least one coefficient pertaining to an edge and assigns to it a different quantized symbol, so as to enhance the edge transition.

In other non-limiting embodiments, the encoder performs quantization in two steps: a first-pass quantization with relatively large stepwidth and relatively small dead zone, with the purpose of grouping neighbouring residual values in clusters, subsequently, the actual quantization with relatively small target quantization stepwidths, detecting a reference dequantization offset for the coefficient group.

According to further non-limiting embodiments, the decoder is configured to generate a progressively larger dequantization offset as the quantization bucket moves from the center of the residual distribution (zero symbol/dead zone) to the two tails of the distribution. In some non-limiting embodiments, the decoder produces for each quantization StepWidth_(i) a StepWidth_Modifier_(i) value dependent on StepWidth_(i). The decoder then produces Decoding_StepWidth_(i)=(StepWidth_(i)+StepWidth_Modifier_(i)). Decoding_StepWidth_(i) is then used instead of StepWidth_(i) in order to perform dequantization.

In some non-limiting embodiments, the encoder signals dequantization offset value δ as encoding metadata within the encoded bitstream. In an embodiment, a decoder detects whether or not a dequantization offset is signalled for a given grouping of quantized coefficients by means of a given signaling bit in the bytestream: if the bit is set to 1, the following byte contains information corresponding to the magnitude of the reference dequantization offset. In other non-limiting embodiments, when no dequantization offset is signaled, the decoder proceeds with calculating a StepWidth_Modifier for each group of transformed coefficient. In other non-limiting embodiments, based on a second signaling bit, the decoder processes the bytestream to produce a reference dequantization offset, and at the same time produces one or more StepWidth_Modifiers to be added to the StepWidth in order to progressively increase the dequantization offset. The StepWidth_Modifier may be useful to avoid artefacts such as mosquito noise.

In some non-limiting embodiments, processing operations and intermediate variables are performed by means of fixed-point integer numbers, avoiding to perform processing operations dependent on floating point numbers.

According to further non-limiting embodiments, the offset values δ_(i) for each quantization bin are produced based on parameters identifying the probability distribution of unquantized values. In a non-limiting embodiment, offset values are calculated by assuming a laplacian (i.e., double-exponential) probability distribution, identified based at least in part on the meta data. In other non-limiting embodiments, offset values are directly decoded from the meta data. In a non-limiting embodiment, the offset value δ is the same for all bins except for the bin centered around the zero value, said offset value δ is added to the center-most edge of the quantization bin for bins corresponding to positive unquantized values (thus reducing the magnitude of the dequantized value with respect to the average of the range of the bin) and subtracted to the average of the bin for bins corresponding to negative unquantized values (thus again reducing the absolute value of the dequantized value with respect to the average of the range of the bin).

In other non-limiting embodiments described herein, the signal processor is configured to implement a stochastic dequantization process (“statistical dithering”, or “statistical dequantization”), reproducing for dequantized values belonging to a same Quantization Bin i a probability distribution similar to that of the original unquantized values that were quantized into that Bin. The signal processor receives meta data indicating a probability distribution for the original unquantized values. Based at least in part on parameters of said probability distribution, on the smallest and largest value of each Bin and on a random-generated value, the signal processor produces a dequantized value {circumflex over (R)}_(ι) for each quantized value Q_(i). In such embodiments, multiple occurrences of a same quantized value Q_(i) may be dequantized into multiple different dequantized values {circumflex over (R)}_(ι). In a non-limiting embodiment, parameters in the meta data indicating the probability distribution for the original unquantized values are assumed to be parameters of a laplacian distribution.

In other non-limiting embodiments described herein, the signal processor processes the received meta data corresponding to quantized data and produces a parameter StepWidth_(i) indicating the size of a Quantization Bin and information corresponding to the probability distribution of the original unquantized data. Based at least in part on said parameter and said information, the signal processor produces the ranges of all of the Quantization Bins to be used to dequantize the received quantized data. In a non-limiting embodiment, the size of the Quantization Bins is non-uniform. In other non-limiting embodiments, the size of the Quantization Bins is non-uniform, and is obtained based at least in part on a lookup table known to the signal processor. In other non-limiting embodiment, dequantization follows a scheme of Dead Zone plus Uniform Step Width (DZ+USW) wherein both DZ and USW are functions of parameter StepWidth_(i). In a non-limiting embodiment, the dequantization offset is also a function of parameter StepWidth, and calculated as f(StepWidth).

According to other non-limiting embodiments described herein, the signal processor pre-calculates a set of parameters based on the received meta data and stores said parameters in a lookup table, so as to speed up the process of dequantization of quantized data.

According to some non-limiting embodiments described herein, information corresponding to the probability distribution of original unquantized values comprises a parameter corresponding to the diversity parameter (“b”) of a laplacian distribution. In other non-limiting embodiments, information corresponding to the probability distribution of original unquantized values comprises parameters corresponding to the probabilities of some Quantization Bins (“Mini Histogram”).

FIG. 9 illustrates a distribution and quantization bins. It can be seen that the optimal dequantization point is neither in the middle nor at the center-most extreme of a quantization bucket. In the hypothesis of a simil-Laplacian distribution of residuals, the dequantization offset should be the substantially the same for every quantization bucket. The optimal average dequantization offset varies with step-width. Smaller step-widths require relatively larger offsets. Bigger step-widths require small offsets. Different groups of transformed coefficients are encoded with different step-widths due to the Quantization Matrix. As such, if an offset is statistically precise for a direction, it may be incorrect for another direction. Non-limiting embodiments described herein modify the offset based on direction Step-Width. Other non-limiting embodiments described herein expand the decoding quantization stepwidth with respect to the one that was used to encode, so as to progressively increase the dequantization offset.

FIG. 10 illustrates a quantisation and dequantization process schematically, as described elsewhere in the present disclosure.

The following sets out a specific implementation of how a quantization and dequantization process may be performed. If the dequantization offset is not specified by the encoder in the signalling (default), the decoder acts conservatively and

-   -   1. Assumes a likely residual distribution (i.e., with relatively         small optimal decoding offsets)     -   2. Applies decoding offset progressively, in the form of         expanding the decoding StepWidth vs. the StepWidth that was used         to encode. This generates a progressively increasing «effective         dequantization offset», very small for quantization bucket 1 and         progressively larger for buckets at the tails of the residual         distribution.

In an example non-limiting embodiment, the target «tail-most bucket» dequantization offset for a given transformed coefficient StepWidth i is calculated with the following formula, which assumes a Laplacian distribution of residuals with Beta=500:

TailMost_Dequant_Offset^(i)=−0.14·ln(StepWidth^(i))+1.22

The StepWidth modifier is then calculated as follows:

${{{Max\_ Num}{\_ Buckets}} = \frac{32768}{{StepWidth}^{i}}}{{StepWidth\_ Modifier}^{i} = {{\frac{{TailMost\_ Dequant}{\_ Offset}^{i}}{{Max\_ Num}{\_ Buckets}} \cdot {StepWidth}^{i}} = {\frac{{TailMost\_ Dequant}{\_ Offset}^{i}}{32768} \cdot \left( {StepWidth}^{i} \right)^{2}}}}$

And finally:

Decoding_StepWidth^(i)=StepWidth^(i)+StepWidth_Modifier^(i)

The formula for the StepWidth modifier may be derived from this formula:

StepWidth^(i)·TailMost_Dequant_Offset^(i)=StepWidth_Modifier^(i)·Max_Num_Buckets

Note that the these formulas are consistent with the example implementation described above.

In other non-limiting embodiments, if the dequantization offset to be used in dequantization is specified by the encoder in the signalling (e.g., by means of toggling a signalling bit) and higher than zero, it is applied by the decoder for dequantization at the specified LOQ Target Step Width («reference dequantization offset»)

If a Quantization Matrix is applied, the dequantization offset for each direction (which is to be decoded with a different step width) is modified according to a formula like one of the following non-limiting examples:

Actual_Dequant_Offset^(i)=−0.14·ln(StepWidth^(i))+Reference_Dequant_Offset+0.14·ln(LOQ_Target_StepWidth)

Actual_Dequant_Offset^(Direction_i)=−0.08·ln(StepWidth^(Direction) ^(i) )+Reference_Dequant_Offset+0.08·ln(LOQ_Target_StepWidth)

FIGS. 11 and 12 illustrate non-limiting example values assuming a perfect Laplacian distribution, at various levels of «b» (beta) parameter. The values assume a beta in the 300-2,000 range.

In a further non-limiting example, a mode is proposed which operates like default, but the target «tailmost_bucket» dequantization offset is calculated as follows:

TailMost_Dequant_Offset^(Direction_i)=−0.08·ln(StepWidth^(Direction) ^(i) )+Reference_Dequant_Offset+0.08·ln(LOQ_Target_StepWidth)

In this non-limiting example, the StepWidth modifier is then calculated as follows:

${{{Max\_ Num}{\_ Buckets}} = \frac{32768}{{StepWidth}^{{Direction}_{i}}}}{{StepWidth\_ Modifier}^{{Direction}_{i}} = {{\frac{{TailMost\_ Dequant}{\_ Offset}^{{Direction\_}i}}{{Max\_ Num}{\_ Buckets}}.{StepWidth}^{{Direction}_{i}}} = {\frac{{TailMost\_ Dequant}{\_ Offset}^{{{Direction}\_}i}}{32768} \cdot \left( {StepWidth}^{{Direction}_{i}} \right)^{2}}}}$

And finally:

Decoding_StepWidth^(Direction) ^(i) =StepWidth^(Direction) ^(i) +StepWidth_Modifier^(Direction) ^(i)

Again, note that a formula for the StepWidth modifier can, in an example, be derived from this formula:

StepWidth^(Direction) ^(i) ·TailMost_Dequant_Offset^(Direction_i)=StepWidth_Modifier^(Direction) ^(i) ·Max_Num_Buckets

FIG. 13 shows a block diagram of a system for implementing processes as described herein.

Further Implementation

In certain embodiments as described herein, in an example implementation, it should be noted that for lossless compression, the following may be implemented:

Where, if stepWidth>16, deadZoneWidthOffset is derived as follows:

deadZoneWidthOffset[x][y]=(((1<<16)−((Aconst*(qm[x+(levelIdxSwap*nTbs)][y]+stepWidthModifier[x][y]))+Bconst)>>1)*(qm[x+(levelIdxSwap*nTbs)][y]+stepWidthModifier[x][y]))>>16

Where, if stepWidth<=16, deadZoneWidthOffset is derived as follows:

deadZoneWidthOffset[x][y]=stepWidth>>1

That is, where the stepwidth is less than 16, a deadzonewidthoffset is not dependent on the matrix of parameters.

Further Example Implementation

The attached appendix below, provides an example, non-limiting, implementation embodying the principles described elsewhere herein. This example can be used to provide context for the features described.

Further Example Encoder and Decoder

FIGS. 25 and 26 respectively show variations of the encoder architecture of FIGS. 1, 3A and 3B and the decoder architecture of FIGS. 2, 5A and 5B.

The encoding process 2500 to create a bitstream is shown in FIG. 14. Firstly, the input sequence 2502 is fed into a first down-sampler 2504, then a second down-sampler 2506 (i.e. consecutive down-samplers that are called down-scalers in the Figure) and is processed according to a chosen scaling mode. The variation of FIG. 14 differs from that of previous examples in that there are additional down-sampling and up-sampling stages prior to the base layer, e.g. an additional down-sampling stage shown as second down-scaler 2506 is possible prior to passing data to a base encoder 2512 and an additional up-sampling stage (shown an as first up-scaler 2508 in FIG. 14) is possible following receipt of decoded data from the base layer. In certain examples, a given scaling mode may be used to turn on and off the down-scaler and up-scaler pairs at each stage. In one case, the scaling mode may indicate a direction of scaling, e.g. as per the horizontal only down-sampling/up-sampling described herein. If the second down-scaler 2506 and the first up-scaler 2508 are turned off, then the spatial scaling resembles that of FIGS. 1, 3A and 3B.

In FIG. 14, as per the previous examples, a base codec is used that produces a base bitstream 2516 according to its own specification. This encoded base may be included as part of a combined bitstream for the present video coding framework.

With or without additional upscaling, a reconstructed base picture, e.g. a decoded version of a base encoded frame, is subtracted at first subtraction component 2520 from a first-order downscaled input sequence in order to generate the sub-layer 1 residuals (the level 1 residual data as descried herein). These residuals form the starting point for the encoding process of the first enhancement layer. Transform component 2521, quantization component 2523 and entropy encoding component 2524 (amongst others) as described herein process the first set of (level 1) residuals to generate (level 1) entropy encoded quantized transform coefficients 2526.

In FIG. 14, as per previous examples, the entropy encoded quantized transform coefficients from sub-layer 1 are processed by an in-loop decoder that performs inverse or decoding operations. These operations simulate a decoding process for the first set of residuals that would be performed at a decoder. In the example of FIG. 14, these comprise an entropy decoding component 2525, an inverse quantization component 2527, an inverse transform component 2528 and a level 1 filter 2530. These may be similar to previously described components. The processed or “decoded” first set of residuals are added to data derived from the output of the base encoder (e.g. as decoded and optionally upscaled) at summation component 2532 to generate a reconstructed frame. In FIG. 14, the reconstructed frame is processed by a second up-scaler 2534. The use of the up-scaler may again depend on a chosen scaling mode. Finally, the residuals for a second sub-layer 2 (which may also be called a L2 layer) are calculated at a second subtraction component 2536 by a subtraction of the input sequence and the upscaled reconstruction. These form a second set of (level 2) residuals and these residuals are also processed by a set of coding components or tools, which include a transform component 2541, a temporal prediction component 2542, a quantization component 2543 and an entropy encoding component 2544. The output is a set of level 2 coefficient layers 2546. As described in other examples, if a temporal mode is activated, an additional temporal prediction may be applied by the temporal prediction component 2542 on the transform coefficients in order to remove certain temporally redundant information and reduce the energy of the level 2 residual stream (e.g. the number of values and the number of non-zero residual values). The entropy encoded quantized transform coefficients of sub-layer 2 as well as a temporal layer 2556 specifying the use of the temporal prediction on a block basis are included in the enhancement bitstream. The temporal layer 2556 may comprise the temporal signalling described with reference to previous examples. It may be entropy encoded by an entropy encoding component 2557. The entropy encoding component 2557 may apply at least run length encoding as discussed with reference to the examples.

The encoder 2500 may be configured with a set of encoder configuration information 2565, e.g. as described with reference to the examples of FIGS. 14A to 14C. This information may be transmitted to a decoder as a set of headers 2566 for the output bitstream. In FIG. 14, a combined bitstream for the encoder may comprise headers 2566, a temporal layer 2556, level 2 (L2) encoded coefficients 2546, level 1 (L1) encoded coefficients 2526 and an encoded base stream 2516.

FIG. 15 shows a variation of a decoder 2600 according to an example. The decoder may comprise a variation of the decoder shown in any one of the other figures herein. The decoder of FIG. 15 may be used together with the encoder of FIG. 14.

First, for the creation of an output sequence of frames, the decoder 2600 analyses the bitstream. As can be seen in FIG. 15, the process can again be divided into three parts.

In order to generate a decoded base picture (e.g. at Layer 0), a base decoder 2618 is fed with the extracted base bitstream 2616. According to the chosen scaling mode, this reconstructed picture may be upscaled by an additional first up-scaler 2608 prior to a summation component 2630 that adds a first set of (level 1) residuals. The input to the summation component 2630 from the first up-scaler 2608 may be referred to as a preliminary intermediate picture.

Following (or in parallel with) the base layer decoding, the enhancement layer bitstream (including the two sublayers of residuals) needs to be decoded. Firstly, the coefficients 2626 belonging to sub-layer 1 (L1) are decoded using inverse versions of the coding components or tools used during the encoding process. Hence, the level 1 coefficient layers 2626 are processed, in turn, by an entropy decoding component 2671, a inverse quantization component 2672, and an inverse transform component 2673. Additionally, a sub-layer 1 (L1) filter 2632 might be applied in order to smooth the boundaries of the transform block (i.e. the coding unit). The output of the sub-layer 1 (L1) decoding process may be referred to as an enhancement sub-layer 1 output. This enhancement sub-layer 1 output is added to the preliminary intermediate picture at the first (lower) summation component 2630, which results in a combined intermediate picture. Again, depending on the scaling mode, a second up-scaler 2687 may be applied and the resulting preliminary output picture produced. The preliminary output picture is provided to the second upper summation component 2658. It has the same dimensions as the overall output picture.

As a final step, the encoded coefficients 2646 for the second enhancement sub-layer 2 are decoded. Again, this uses a set of inverse coding components or tools as described in other examples herein. In FIG. 15, these components include an entropy decoding component 2681, an inverse quantization component 2682, and an inverse transform component 2683. If a temporal mode is activated, then a temporal prediction component 2685 may apply temporal prediction. Temporal prediction may be applied at any point within the second enhancement sub-layer 2. In one case, it is applied to the quantized transform coefficients. Temporal prediction may be applied based on signalling received as the temporal layer 2656. In FIG. 15, the temporal layer 2656 is decoded by an entropy decoding component 2690 (e.g. may be run-length decoded). The output of the temporal prediction is provided into the second upper summation component 2658 as an enhancement sub-layer 2 output. It is then added to the preliminary output picture by said summation component 2658 to form a combined output picture 2660 as a final output of the decoding process.

Again, the decoding process may be controlled according to a decoder configuration 2692 as transmitted within headers 2666 of the bit stream.

As described with reference to the above examples, unlike comparative scalable codecs, the new approaches described herein may be completely agnostic of the codec used to encode the lower layer. This is because the upper layer is decodable without any information about the lower layer. As shown in FIG. 15, a decoder receives multiple streams generated by the encoder. These may be five or so streams that include: a first encoded stream (encoded base) that is produced by feeding a base codec (e.g., AVC, HEVC, or any other codec) with a down-sampled version of the input video; a second encoded stream (level 1 coefficient layers) that is produced by processing the residuals obtained by taking the difference between the reconstructed base codec video and the down-sampled version of the input video (level 1 residuals); a third encoded stream (level 2 coefficient layers) that is produced by processing the residuals obtained by taking the difference between an up-sampled version of a corrected version of the reconstructed base coded video and the input video (level 2 residuals); a fourth encoded stream (e.g. in the form of a temporal layer) that is produced from the temporal processing to instruct the decoder; and a fifth stream (headers) that are produced for configuring the decoder. The encoded base stream is decoded by a base decoder implementing a decoding algorithm corresponding to the encoding algorithm implemented by the base codec used in the encoder, and the output of this is a decoded base. Separately, and independently, the level 1 coefficient groups are decoded in order to obtain level 1 residual data. Further, separately and independently, the level 2 coefficient groups are decoded in order to obtain level 2 residual data. The decoded base, the level 1 residual data and the level 2 residual data are then combined. In particular, the decoded base is combined with the level 1 residuals data to generate an intermediate picture. The intermediate picture may be then up-sampled and further combined with the level 2 residual data.

Moreover, the new approach uses an encoding and decoding process which processes the picture without using any inter-block prediction. Rather, it processes the picture by transforming an N×N block of picture elements (e.g., 2×2 or 4×4) and processing the blocks independently from each other. This results in efficient processing as well as in no-dependency from neighbouring blocks, thus allowing the processing of the picture to be parallelised.

In general summary, with reference to FIG. 15, there is shown there is shown a non-limiting exemplary embodiment. In FIG. 15, an exemplary decoding module 2600 is depicted. The decoding module 2600 receives a plurality of input bitstreams, comprising encoded base 2616, level 1 coefficient groups 2626, level 2 coefficient groups 2646, a temporal coefficient group 2656 and headers 2666.

In general, the decoding module 2600 processes two layers of data. A first layer, namely the base layer, comprises a received data stream 2616 which includes the encoded base. The encoded base 2616 is then sent to a base decoding module 2618, which decodes the encoded base 2616 to produce a decoded base picture. The base decoding may be a decoder implementing any existing base codec algorithm, such as AVC, HEVC, AV1, VVC, EVC, VC-6, VP9, etc. depending on the encoded format of the encoded base.

A second layer, namely the enhancement layer, is further composed of two enhancement sublayers. The decoding module receives a first group of coefficients, namely level 1 coefficient groups 2626, which are then passed to an entropy decoding module 2671 to generate decoded coefficient groups. These are then passed to an inverse quantization module 2672, which uses one or more dequantization parameters to generate dequantized coefficient groups. These are then passed to an inverse transform module 2673 which performs an inverse transform on the dequantized coefficient groups to generate residuals at enhancement sublayer 1 (level 1 residuals). The residuals may then be filtered by a smoothing filter 2632. The level 1 residuals (i.e., the decoded first enhancement sublayer) is applied to a processed output of the base picture.

The decoding module receives a second group of coefficients, namely level 2 coefficient groups 2646, which are then passed to an entropy decoding module 2681 to generate decoded coefficient groups. These are then passed to an inverse quantization module 2682, which uses one or more dequantization parameters to generate dequantized coefficient groups. The dequantization parameters used for the enhancement sublayer 2 may be different from the dequantization parameters used for the enhancement sublayer 1. The dequantized coefficient groups are then passed to an inverse transform module 2683 which performs an inverse transform on the dequantized coefficient groups to generate residuals at enhancement sublayer 2 (level 2 residuals).

Implementation

At both the encoder and decoder, for example implemented in a streaming server or client device or client device decoding from a data store, methods and processes described herein can be embodied as code (e.g., software code) and/or data. The encoder and decoder may be implemented in hardware or software as is well-known in the art of data compression. For example, hardware acceleration using a specifically programmed Graphical Processing Unit (GPU) or a specifically designed Field Programmable Gate Array (FPGA) may provide certain efficiencies. For completeness, such code and data can be stored on one or more computer-readable media, which may include any device or medium that can store code and/or data for use by a computer system. When a computer system reads and executes the code and/or data stored on a computer-readable medium, the computer system performs the methods and processes embodied as data structures and code stored within the computer-readable storage medium. In certain embodiments, one or more of the steps of the methods and processes described herein can be performed by a processor (e.g., a processor of a computer system or data storage system).

Generally, any of the functionality described in this text or illustrated in the figures can be implemented using software, firmware (e.g., fixed logic circuitry), programmable or nonprogrammable hardware, or a combination of these implementations. The terms “component” or “function” as used herein generally represents software, firmware, hardware or a combination of these. For instance, in the case of a software implementation, the terms “component” or “function” may refer to program code that performs specified tasks when executed on a processing device or devices. The illustrated separation of components and functions into distinct units may reflect any actual or conceptual physical grouping and allocation of such software and/or hardware and tasks.

APPENDIX

The following sets out an example, non-limiting, implementation embodying the principles described elsewhere herein. This example can be used to provide context for the features described.

Syntax and Semantics

The syntax tables specify a superset of the syntax of all allowed bitstreams. Additional constraints on the syntax may be specified, either directly or indirectly, as appropriate.

NOTE An actual decoder should implement some means for identifying entry points into the bitstream and some means to identify and handle non-conforming bitstreams. The methods for identifying and handling errors and other such

Process Payload—Picture Configuration

Syntax Descriptor process_payload_picture_config (payload_size) {  no_enhancement_bit_flag u(1)  if (no_enhancement_bit_flag == 0) {   quant_matrix_mode u(3)   dequant_offset_signalled_flag u(1)   picture_type_bit_flag u(1)   temporal_refresh_bit_flag u(1)   step_width_level1_enabled_flag u(1)   step_width_level2 u(15)   dithering_control_flag u(1)  } else {   reserved_zeros_4bit u(4)   picture_type_bit_flag u(1)   temporal_refresh_bit_flag u(1)   temporal_signalling_present_flag u(1)  }  if (picture_type_bit_flag == 1) {   field_type_bit_flag u(1)   reserved_zeros_7bit u(7)  }  if (step_width_level1_  enabled_flag == 1) {   step_width_level1 u(15)   level1_filtering_enabled_flag u(1)  }  if (quant_matrix_mode == 2 ||  quant_matrix_mode == 3 || quant_matrix_mode == 5) {   for(layerIdx = 0; layerIdx <    nLayers; layerIdx++) {    qm_coefficient_0[layerIdx] u(8)   }  }  if (quant_matrix_mode == 4 ||   quant_matrix_mode == 5) {   for(layerIdx = 0; layerIdx <    nLayers; layerIdx++) {    qm_coefficient_1[layerIdx] u(8)   }  }  if (dequant_offset_signalled_flag) {   dequant_offset_mode_flag u(1)   dequant_offset u(7)  }  if (dithering_control_flag == 1) {   dithering_type u(2)   reserverd_zero u(1)   if (dithering_type != 0) {    dithering_strength u(5)   } else {    reserved_zeros_5bit u(5)   }  } }

Data Block Unit Picture Configuration Semantics

no_enhancement_bit_flag specifies that there are no enhancement data for all layerIdx<nLayers in the picture.

quant_matrix_mode specifies which quantization matrix to be used in the decoding process in accordance with the table below. When quant_matrix_mode is not present, it is inferred to be equal to 0.

Quantization Matrix

quant_matrix_mode Value of type 0 each enhancement  sub-layer uses the  matrices used for   the previous frame, unless the current  picture is an IDR picture, in which  case both enhancement  sub-layers use  default matrices 1 both enhancement   sub-layers use default matrices 2 one matrix of modifiers  is signalled and  should be used on  both residual plane 3 one matrix of modifiers   is signalled and  should be used on  enhancement sub- layer 2 residual plane 4 one matrix of  modifiers is  signalled and should   be used on enhancement sub- layer 1 residual plane 5 two matrices of  modifiers are signalled- the first one for   enhancement sub-layer 2 residual plane, the  second for enhancement  sub-layer 1 residual plane 6-7 Reserved

dequant_offset_signalled_flag specifies if the offset method and the value of the offset parameter to be applied when dequantizing is signalled. If equal to 1, the method for dequantization offset and the value of the dequantization offset parameter are signalled. When dequant_offset_signalled_flag is not present, it is inferred to be equal to 0.

picture_type_bit_flag specifies whether the encoded data are sent on a frame basis (e.g., progressive mode or interlaced mode) or on a field basis (e.g., interlaced mode) in accordance with the table below Error! Reference source not found.

Picture Type

picture_type_bit_flag Value of type 0 Frame 1 Field

field_type_bit_flag specifies, if picture_type is equal to 1, whether the data sent are for top or bottom field in accordance with the table below Error! Reference source not found.

Field Type

field_type_bit_flag Value of type 0 Top 1 Bottom

temporal_refresh_bit_flag specifies whether the temporal buffer should be refreshed for the picture. If equal to 1, the temporal buffer should be refreshed. For an IDR picture, temporal_refresh_bit_flag shall be set to 1.

temporal_signalling_present_flag specifies whether the temporal signalling coefficient group is present in the bitstream. When temporal_signalling_present_flag is not present, it is inferred to be equal to 1 if temporal_enabled_flag is equal to 1 and temporal_refresh_bit_flag is equal to 0, otherwise it is inferred to be equal to 0.

step_width_level2 specifies the value of the stepwidth value to be used when decoding the encoded residuals in enhancement sub-layer 2 for the luma plane. The stepwidth value to be used when decoding the encoded residuals in enhancement sub-layer 2 for the chroma planes shall be computed as Clip3(1, 32,767, ((step_width_level2*chroma_step_width_multiplier)>>6)).

step_width_level1_enabled_flag specifies whether the value of the stepwidth to be used when decoding the encoded residuals in the enhancement sub-layer 1 is a default value or is signalled. It should be either 0 (default value) or 1 (value signalled by step_width_level1). The default value is 32,767. When step_width_level1_enabled_flag is not present, it is inferred to be equal to 0.

dithering_control_flag specifies whether dithering should be applied. It should be either 0 (dithering disabled) or 1 (dithering enabled). When dithering_control_flag is not present, it is inferred to be equal to 0.

step_width_level1 specifies the value of the stepwidth value to be used when decoding the encoded residuals in enhancement sub-layer 1.

level1_filtering_enabled_flag specifies whether the level1 deblocking filter should be used. It should be either 0 (filtering disabled) or 1 (filtering enabled). When level1_filtering_enabled_flag is not present, it is inferred to be equal to 0.

qm_coefficient_0[layerIdx] specifies the values of the quantization matrix scaling parameter when quant_matrix_mode is equal to 2, 3 or 5.

qm_coefficient_1[layerIdx] specifies the values of the quantization matrix scaling parameter for when quant_matrix_mode is equal to 4 or 5.

dequant_offset_mode_flag specifies the method for applying dequantization offset. If equal to 0, the default method applies, using the signalled dequant_offset as parameter. If equal to 1, the constant-offset method applies, using the signalled dequant_offset parameter.

dequant_offset specifies the value of the dequantization offset parameter to be applied. The value of the dequantization offset parameter should be between 0 and 127, inclusive.

dithering_type specifies what type of dithering is applied to the final reconstructed picture according to the table below Error! Reference source not found.

Dithering

dithering_type Value of type 0 None 1 Uniform 2-3 reserved

dithering_strength specifies a value between 0 and 31.

Decoding Process

The decoding process is specified such that all decoders that conform to a specified profile and level will produce numerically identical cropped decoded output pictures when invoking the decoding process associated with that profile for a bitstream conforming to that profile and level. Any decoding process that produces identical cropped decoded output pictures to those produced by the process described herein (with the correct output order or output timing, as specified) conforms to the decoding process requirements.

General Decoding Process for an L-2 Encoded Data Block

Inputs to this process are:

a sample location (xTb0, yTb0) specifying the top-left sample of the current transform block relative to the top-left sample of the current picture, a variable nTbS specifying the size of the current transform block derived in subclause Error! Reference source not found. from the value of variable transform_type (nTbS=2 if transform_type is equal to 0 and nTbS=4 if transform_type is equal to 1),

a variable temporal_enabled_flag as derived elsewhere and a variable temporal_refresh_bit_flag as derived elsewhere, a variable temporal_signalling_present_flag as derived in elsewhere and temporal_step_width_modifier as specified elsewhere

an array recL2ModifiedUpsampledSamples of a size (nTbS)×(nTbS) specifying the upsampled reconstructed samples resulting from process specified elsewhere of the current block,

an array TransformCoeffQ of a size (nTbS)×(nTbS) specifying L-2 entropy decoded quantized transform coefficient,

if variable temporal_signalling_present_flag is equal to 1 and temporal_tile_intra_signalling_enabled_flag is equal to 1, a variable TransformTempSig corresponding to the value in TempSigSurface at the position (xTb0>>nTbs, yTb0>>nTbs) and if in addition temporal_tile_intra_signalling_enabled_flag is set to 1, a variable TileTempSig corresponding to the value in TempSigSurface at the position ((xTb0% 32)*32, (yTb0% 32)*32),

stepWidth value derived elsewhere from the value of variable step_width_level2, a variable IdxPlanes specifying to which plane the transform coefficients are belonging to.

Output to this process is the (nTbS)×(nTbS) array of L-2 residuals resL2Residuals with elements resL2Residuals[x][y].

The sample location (xTbP, yTbP) specifying the top-left sample of the current transform block relative to the top-left sample of the current picture is derived as follows:

(xTbP,yTbP)=(IdxPlanes==0)?(xTb0,yTb0):(xTb0>>ShiftWidthC,yTb0>>ShiftHeightC)

P can be related to either luma or chroma plane depending to which plane the transform coefficients belong. Where ShiftWidthC and ShiftHeightC are specified elsewhere.

If no_enhancement_bit_flag is set to 0, then the following ordered steps apply:

If variable temporal_enabled_flag is equal to 1 and temporal_refresh_bit_flag is equal to 0 the temporal prediction process as specified elsewhere is invoked with the luma location (xTbY, yTbY), the transform size set equal to nTbS, a variable TransformTempSig and a variable TileTempSig as inputs and the output is an array tempPredL2Residuals of a size (nTbS)×(nTbS).

If variable temporal_enabled_flag is equal to 1 and temporal_refresh_bit_flag is equal to 1 the array tempPredL2Residuals of a size (nTbS)×(nTbS) is set to contain only zeros.

If variable temporal_enabled_flag is equal to 1, temporal_refresh_bit_flag is equal to 0 and temporal_tile_intra_signalling_enabled_flag is equal to 1 (subclause 0) and TransformTempSig is equal to 0 the variable stepWidth is modified to Floor(stepWidth*(1−(Clip3(0, 0.5, (temporal_step_width_modifier/255))))).

The dequantization process as specified elsewhere is invoked with the transform size set equal to nTbS, the array TransformCoeffQ of a size (nTbS)×(nTbS), and the variable stepWidth as inputs, and the output is an (nTbS)×(nTbS) array dequantCoeff.

The transformation process as specified elsewhere is invoked with the luma location (xTbY, yTbY), the transform size set equal to nTbS, the array dequantCoeff of a size (nTbS)×(nTbS) as inputs, and the output is an (nTbS)×(nTbS) array resL2Residuals.

If variable temporal_enabled_flag is equal to 1 the array of tempPredL2Residuals of a size (nTbS)×(nTbS) is added to the (nTbS)×(nTbS) array resL2Residuals and resL2Residuals array is stored to the temporalBuffer at the luma location (xTbY, yTbY).

If no_enhancement_bit_flag is set to 1, the following ordered steps apply:

If variable temporal_enabled_flag is equal to 1, temporal_refresh_bit_flag is equal to 0 and variable temporal_signalling_present_flag is equal to 1, the temporal prediction process as specified elsewhere is invoked with the luma location (xTbY, yTbY), the transform size set equal to nTbS, a variable TransformTempSig and a variable TileTempSig as inputs and the output is an array tempPredL2Residuals of a size (nTbS)×(nTbS).

If variable temporal_enabled_flag is equal to 1, temporal_refresh_bit_flag is equal to 0 and variable temporal_signalling_present_flag is equal to 0, the temporal prediction process as specified elsewhere is invoked with the luma location (xTbY, yTbY), the transform size set equal to nTbS, a variable TransformTempSig set equal to 0 and a variable TileTempSig set equal to 0 as inputs and the output is an array tempPredL2Residuals of a size (nTbS)×(nTbS).

If variable temporal_enabled_flag is equal to 1 and temporal_refresh_bit_flag is equal to 1 the array tempPredL2Residuals of a size (nTbS)×(nTbS) is set to contain only zeros.

If variable temporal_enabled_flag is equal to 1 the array of tempPredL2Residuals of a size (nTbS)×(nTbS) is stored in the (nTbS)×(nTbS) array resL2Residuals and resL2Residuals array is stored to the temporalBuffer at the luma location (xTbY, yTbY).

Else, the array resL2Residuals of a size (nTbS)×(nTbS) is set to contain only zeros.

The picture reconstruction process for each plane as specified in subclause Error! Reference source not found. is invoked with the transform block location (xTb0, yTb0), the transform block size nTbS, the variable IdxPlanes, the (nTbS)×(nTbS) array resL2Residuals, and the (xTbY)×(yTbY) recL2ModifiedUpsampledSamples as inputs.

Decoding Process for the Dequantization

Every group of transform coefficient passed to this process belongs to a specific plane and enhancement sub-layer. They have been scaled using a uniform quantizer with deadzone. The quantizer can use a non-centered dequantization offset.

Scaling Process for Transform Coefficients

Inputs to this process are:

a variable nTbS specifying the size of the current transform block (nTbS=2 if transform_type is equal to zero and nTbS=4 if transform_type is equal to 1), an array TransformCoeffQ of size (nTbS)×(nTbS) containing entropy decoded quantized transform coefficient,

a variable stepWidth specifying the step width value parameter,

a variable levelIdx specifying the index of the enhancement sub-layer (with levelIdx=1 for enhancement sub-layer 1 and levelIdx=2 for enhancement sub-layer 2),

a variable dQuantOffset specifying the dequantization offset and variable dequant_offset),

if quant_matrix_mode is different from 0, an array QmCoeff0 of size 1×nTbS2 (equal to array variable qm_coefficient_0) and further, if quant_matrix_mode is equal to 4, an array QmCoeff1 of size 1×nTbS2 (equal to array qm_coefficient_1), if nTbS==2, an array QuantScalerDDBuffer of size (3*nTbS)×(nTbS) containing the scaling parameters array used in the previous picture;

if nTbS==4, an array QuantScalerDDSBuffer of size (3*nTbS)×(nTbS) containing the scaling parameters array used in the previous picture.

Output of this process is the (nTbS)×(nTbS) array d of dequantized transform coefficients with elements d[x][y] and the updated array QuantMatrixBuffer.

For the derivation of the scaled transform coefficients d[x][y] with x=0 . . . nTbS−1, y=0 . . . nTbS−1, and given matrix qm[x][y] as specified in subclause 8.6.2, the following formula is used:

d[x][y]=(TransformCoeffQ[x][y]*((qm[x+(levelIdxSwap*nTbS)][y]+stepWidthModifier[x][y])+appliedOffset[x][y])  (1)

Derivation of Dequantization Offset and Stepwidth Modifier

The variables appliedOffset[x][y] and stepWidthModifier[x][y] are derived as follows:

if (dequant_offset_signalled_flag == 0) {  stepWidthModifier [x][y] = ((((Floor(−Cconst * Ln (qm[x + (levelldxSwap * nTbS)][y]))) + Dconst) *  (qm[x + (levelldxSwap * nTbS)][y]2))) / 32768) >> 16  if (TransformCoeffQ[x][y] < 0)   appliedOffset [x][y] = (−1 * (−deadZoneWidthOffset [x][y]))  else if (TransformCoeffQ [x][y] > 0)    appliedOffset [x][y] = −deadZoneWidthOffset [x][y]  else   appliedOffset [x][y] = 0 } else if (dequant_offset_signalled_flag == 1) && (dequant_offset_mode_flag ==1) {  stepWidthModifier [x][y] = 0  if (TransformCoeffQ[x][y] <0)   appliedOffset = (−1 * (dQuantOffsetActual [x][y] − deadZoneWidthOffset [x][y]))  else if (TransformCoeffQ [x][y] > 0)   appliedOffset [x][y] = dQuantOffsetActual [x][y] − deadZoneWidthOffset [x][y]  else   appliedOffset [x][y] = 0} } else if (dequant_offset_signalled_flag == 1) && (dequant_offset_mode_flag == 0) {  stepWidthModifier [x][y] = (Floor((dQuantOffsetActual [x][y]) * (qm[x + (levelldxSwap * nTbS)][y]))   / 32768)  if (TransformCoeffQ[x][y] <0)   appliedOffset = (−1 * (−deadZoneWidthOffset [x][y]))  else if (TransformCoeffQ [x][y] > 0)   appliedOffset [x][y] = −deadZoneWidthOffset [x][y]  else   appliedOffset [x][y] = 0 }

Where, if stepWidth>16, deadZoneWidthOffset is derived as follows:

deadZoneWidthOffset[x][y]=(((1<<16)−((Aconst*(qm[x+(levelIdxSwap*nTbs)][y]+stepWidthModifier[x][y]))+Bconst)>>1)*(qm[x+(levelIdxSwap*nTbs)][y]+stepWidthModifier[x][y]))>>16

Where, if stepWidth<=16, deadZoneWidthOffset is derived as follows:

deadZoneWidthOffset[x][y]=stepWidth>>1

Where:

Aconst=39

Bconst=126484

Cconst=5242

Dconst=99614

Where dQuantOffsetActual[x][y] is computed as follows:

  if (dequant_offset == 0)    dQuantOffsetActual [x][y] = dQuantOffset else {    if (dequant_offset_mode_flag == 1)       dQuantOffsetActual [x][y] = ((Floor(−Cconst * Ln(qm[x + (levelIdxSwap * nTbs)][y]) +            (dQuantOffset << 9) + Floor(Cconst* Ln(StepWidth)))) * (qm[x + (levelIdxSwap * nTbs)][y])) >> 16    else if (dequant_offset_mode_flag == 0)       dQuantOffsetActual [x][y] = ((Floor(−Cconst * Ln(qm[x + (levelIdxSwap* nTbs)][y]) +            (dQuantOffset << 11) + Floor(Cconst * Ln(StepWidth)))) * (qm[x + (levelIdxSwap * nTbs)][y]))            >>6 }

Where levelIdxSwap is derived as follows:

  if (levelIdx == 2)    levelIdxSwap = 0 else    levelIdxSwap = 1

Derivation of Quantization Matrix

The quantization matrix qm[x][y] contains the actual quantization step widths to be used to decode each coefficient group.

  if (levelIdx == 2) {    if (scaling_mode_level2 == 1) {        for (x = 0; x < nTbS, x++) {            for (y = 0; y < nTbs, y++)                qm [x][y] = qm_p [x][y]        }    } else {        for (x = 0; x < nTbS, x++) {            for (y = 0; y < nTbS, y++)                qm [x][y] = qm_p [x + nTbS][y]        }    } } else {    for (x = 0; x < nTbS, x++) {        for (y = 0; y < nTbs, y++)            qm [x][y] = qm_p [x + (2 * nTbS)][y]    } }

Where qm_p[x][y] is computed as follows:

  if (nTbs == 2) {    for (x = 0; x < 6; x++) {       for (y = 0; y < nTbs, y++)          qm_p[x][y] = (Clip3 (0, (3 << 16),[(QuantScalerDDBuffer [x][y] * stepWidth) + (1 << 16)]) *             stepWidth) >> 16    } } else {    for (y = 0; y < 12; y++) {       for (x = 0; x < nTbs, x++)          qm_p[x][y]   =   (Clip3   (0,   (3   << 16),[(QuantScalerDDSBuffer [x][y] * stepWidth) + (1 << 16)]) *          stepWidth) >> 16    } }

And where QuantScalerDDBuffer[x][y] is derived elsewhere and QuantScalerDDSBuffer[x][y] is derived elsewhere.

Derivation of Scaling Parameters for 2×2 Transform

If the variable nTbS is equal to 2, the default scaling parameters are as follows:

  default_scaling_dd[x][y] =    {    { 0, 2 }    { 0, 0 }    { 32, 3 )    { 0, 32 }    { 0, 3 }    { 0, 32 }    }

As a first step, the array QuantScalerDDBuffer[x][y] is initialized as follows:

If the current picture is an IDR picture, QuantScalerDDBuffer[x][y] is initialized to be equal to default_scaling_dd[x][y]. If the current picture is not an IDR picture, the QuantScalerDDBuffer[x][y] matrix is left unchanged.

Following initialization, based on the value of quant_matrix_mode the array QuantScalerDDBuffer[x][y] is processed as follows:

If the quant_matrix_mode is equal to 0 and the current picture is not an IDR picture, the QuantScalerDDBuffer[x][y] is left unchanged.

If quant_matrix_mode is equal to 1, the QuantScalerDDBuffer[x][y] is equal to the default_scaling_dd[x][y].

If quant_matrix_mode is equal to 2, the QuantScalerDDBuffer[x][y] is modified as follows:

  for (MIdx = 0; MIdx < 3; MIdx++)    for (x = 0; x < 2; x++)       for (y = 0; y < 2; y++)          QuantScalerDDBuffer [x + (MIdx * 2)][y] = QmCoeff0[(x *2) + y]

If quant_matrix_mode is equal to 3, the QuantScalerDDBuffer[x][y] is modified as follows:

  for (MIdx = 0; MIdx < 2; MIdx++)    for (x = 0; x < 2; x++)       for (y = 0; y < 2; y++)          QuantScalerDDBuffer [x + (MIdx * 2)][y] = QmCoeff0 [(x *2) + y]

If quant_matrix_mode is equal to 4, the QuantScalerDDBuffer[x][y] is modified as follows:

  for (x = 0; x < 2; x++)    for (y = 0; y < 2; y++)       QuantScalerDDBuffer [x + 4][y] =       QmCoeff1[(x * 2) + y]

If quant_matrix_mode is equal to 5, the QuantScalerDDBuffer is modified as follows:

  for (MIdx = 0; MIdx < 2; MIdx ++)    for (x = 0; x < 2; x++)       for (y = 0; y < 2; y++)          QuantScalerDDBuffer [x + (M Idx * 2)][y] = QmCoeff0[(x * 2) + y]    for (x = 4, x < 6; x++)       for (y = 0; y < 2; y++)          QuantScalerDDBuffer [x][y] = QmCoeff1[(x * 2) + y]

Derivation of Scaling Parameters for 4×4 Transform

If the variable nTbS is equal to 4, the default scaling parameters are as follows:

  default_scaling_dds[x][y] =    {    { 13, 26, 19, 32 }    { 52, 1,78, 9 }    { 13, 26, 19, 32 }    { 150, 91, 91, 19 }    { 13, 26, 19, 32 }    { 52, 1,78, 9 }    { 26,72, 0, 3 }    { 150, 91, 91, 19 }    { 0, 0, 0, 2 }    { 52, 1,78, 9 }    { 26,72, 0, 3 }    { 150, 91, 91, 19 }    }

As a first step, the array QuantScalerDDSBuffer[ ][ ] is initialized as follows:

If the current picture is an IDR picture, QuantScalerDDSBuffer[x][y] is initialized to be equal to default_scaling_dds[x][y]. If the current picture is not an IDR picture, the QuantScalerDDSBuffer[x][y] matrix is left unchanged.

Following initialization, based on the value of quant_matrix_mode the array QuantScalerDDSBuffer[x][y] is processed as follows:

If the quant_matrix_mode is equal to 0 and the current picture is not an IDR picture, the QuantScalerDDSBuffer is left unchanged.

If quant_matrix_mode is equal to 1, the QuantScalerDDSBuffer is equal to the default_scaling_dds[x][y].

If quant_matrix_mode is equal to 2, the QuantScalerDDSBuffer is modified as follows:

  for (MIdx = 0; MIdx < 3; MIdx++)    for (x = 0; x < 4; x++)       for (y = 0; y < 4; y++)          QuantScalerDDSBuffer [x + (MIdx * 4)][y] = QmCoeff0[(x * 4) + y]

If quant_matrix_mode is equal to 3, the QuantScalerDDSBuffer is modified as follows:

  for (MIdx = 0; MIdx < 2; MIdx++)    for (x = 0; x < 4; x++)       for (y = 0; y < 4; y++)          QuantScalerDDSBuffer [x + (MIdx * 4)][y] = QmCoeff0[(x * 4) + y]

If quant_matrix_mode is equal to 4, the QuantScalerDDSBuffer is modified as follows:

  for (x = 0; x < 4; x++)    for (y = 0; y < 4; y++)       QuantScalerDDS Buffer [x + 8][y] =       QmCoeffl[(x * 4) + y]

If quant_matrix_mode is equal to 5, the QuantScalerDDSBuffer is modified as follows:

  for (MIdx = 0; MIdx < 2; MIdx++)    for (x = 0; x < 4; x++)       for (y = 0; y < 4; y++)          QuantScalerDDSBuffer [x + (MIdx * 4)][y] = QmCoeff0[(x * 4) + y]    for (x = 8, x < 12; x++)       for (y = 0; y < 4; y++)          QuantScalerDDSBuffer [x][y] = qm_coefficient_1[(x * 4) + y] 

1-141. (canceled)
 142. A method of encoding an input signal into a plurality of encoded streams, wherein the encoded streams may be combined to reconstruct the input signal, the method comprising: receiving an input signal; downsampling the input signal to create a downsampled signal; instructing an encoding of the downsampled signal using a base encoder to create a base encoded stream; instructing a decoding of the base encoded stream using a base decoder to generate a reconstructed signal; comparing the reconstructed signal to the input video to create a set of residuals; and, encoding the set of residuals to create a level encoded stream, including: applying a transform to the set of residuals to create a set of transformed coefficients; applying a quantization operation to the set of transformed coefficients to create a set of quantized coefficients; and applying an encoding operation to the quantized coefficients.
 143. A method according to claim 142, wherein the set of residuals is a first set of residuals and the step of comparing comprises comparing the reconstructed signal to the downsampled signal to create the first set of residuals such that the encoded stream is a first level encoded stream, the method further comprising: decoding the first set of residuals to generate a decoded first set of residuals; correcting the reconstructed signal using the decoded first set of residuals to generate a corrected reconstructed signal; upsampling the corrected reconstructed signal to generate an up-sampled reconstructed signal; comparing the up-sampled reconstructed signal to the input signal to create a second set of residuals; and encoding the second set of residuals to create a second level encoded stream, including: applying a transform to the second set of residuals to create a second set of coefficients; applying a quantization operation to the second set of coefficients to create a second set of quantized coefficients; and applying an encoding operation to the second set of quantized coefficients.
 144. A method according to claim 142, wherein the quantization operation further comprises applying a bin folding operation to the set of coefficients, the bin folding operation comprising each coefficient that has a value in excess of a predetermined maximum value being quantized so as to have a quantized value corresponding to a first quantization bin of a plurality of quantization bins having a defined step width, the maximum value being defined by an upper limit of the first quantization bin.
 145. A method according to claim 142, wherein the quantization operation further comprises using a quantization offset, wherein the using the quantization offset comprises applying the quantization offset to a plurality of quantization bins having a defined step width to adjust each of the values to which one or more of the said plurality of quantization bins correspond by the value of the quantization offset.
 146. A method according to any claim 145, wherein the value of the quantization offset is varied based upon data indicating operating conditions under which the encoding is being performed
 147. A method according to claim 142, wherein the quantization operation further comprises using a quantization offset, and wherein the method comprises deriving from a single reference dequantization offset value δ, corresponding to the signaled reference StepWidth for a given data layer, a respective dequantization offset value δ_(i) for each of the encoded coefficient groups, said offset value δ_(i) being dependent at least in part on the reference offset δ and on quantization StepWidth_(i) of set of coefficients i.
 148. A method according to claim 142, wherein the quantization operation further comprises using a quantization offset, and wherein the offsets for all quantization step widths are derived from a reference step width by according to the relationship: Dequantization_Offsetk=f(StepWidth_(k)), and the relationship: Actual_Dequantization_Offset_(i)=Reference_Dequantization_Offset+f(StepWidth_(i))−f(Reference_StepWidth).
 149. A method according to claim 148, wherein f(StepWidth) is a logarithmic relationship.
 150. A method according to claim 142, further comprising transmitting the plurality of encoded streams with signalling information indicating whether or not a dequantization offset is to be applied when performing a dequantization operation.
 151. A method according to claim 142, wherein the quantization operation further comprises using a quantization offset, wherein the offset is one of: at least a function of a quantization stepwidth; or a function of a probability distribution of the values prior to quantization.
 152. A method of decoding an encoded stream into a reconstructed output signal, the method comprising: receiving a first output signal decoded from a first base encoded stream according to a first codec; receiving a level encoded stream; decoding the level encoded stream to obtain a set of residuals; and, combining the set of residuals with the first output signal to generate a reconstructed signal, wherein the decoding the level encoded stream comprises: decoding a set of quantized coefficients from the level encoded stream; dequantizing the set of quantized coefficients.
 153. A method according to claim 152, wherein: the level encoded stream is a first level encoded stream; the set of quantized coefficients are a first set of quantized coefficients; and, the set of residuals are a first set of residuals, and wherein the method further comprises: receiving a second level encoded stream; decoding the second level encoded stream to obtain a second set of residuals; and combining the second set of residuals with an upsampled version of the reconstructed signal to generate a reconstruction of an original resolution input signal, wherein decoding the second level encoded stream comprises: decoding a second set of quantized coefficients from the second level encoded stream; dequantizing the second set of quantized coefficients.
 154. A method according to claim 152, wherein the dequantizing uses linear quantizer with a non-centred de-quantization offset.
 155. A method according to claim 154, wherein the value of the dequantization offset is received from an encoder, and wherein the received offset value offset is added to a received quantized value prior to dequantization based on a stepwidth.
 156. A method according to claim 152, wherein the dequantizating further comprises using a dequantization offset, wherein the dequantization offset is a function of a quantization step width, and wherein the using the dequantization offset comprises applying the dequantization offset to a plurality of quantization bins having a defined step width to adjust each of the values to which one or more of the said plurality of quantization bins correspond by the value of the dequantization offset.
 157. A method according to claim 152, wherein the dequantizating further comprises using a dequantization offset, and wherein the value of the dequantization offset is varied based upon data indicating operating conditions under which the encoding is being performed.
 158. A method according to claim 152, comprising deriving from a single reference dequantization offset value δ, corresponding to a signaled reference StepWidth for a given data layer, a respective dequantization offset value δ_(i) for each of the encoded coefficient groups, said offset value δ_(i) being dependent at least in part on the reference offset δ and on quantization StepWidth_(i) of set of coefficients i.
 159. A method according to claim 152, wherein the dequantizating further comprises using a dequantization offset, and wherein dequantization offsets for all quantization step widths are derived from a reference step width by according to the relationship: Dequantization_Offset_(k) =f(StepWidth_(k)), and the relationship: Actual_Dequantization_Offset_(i)=Reference_Dequantization_Offset+f(StepWidth_(i))−f(Reference_StepWidth), wherein f(StepWidth) is a logarithmic relationship.
 160. An encoder configured to: receive an input signal; downsample the input signal to create a downsampled signal; instruct an encoding of the downsampled signal using a base encoder to create a base encoded stream; instruct a decoding of the base encoded stream using a base decoder to generate a reconstructed signal; compare the reconstructed signal to the input video to create a set of residuals; and, encode the set of residuals to create a level encoded stream, including being configured to: apply a transform to the set of residuals to create a set of transformed coefficients; apply a quantization operation to the set of transformed coefficients to create a set of quantized coefficients; and apply an encoding operation to the quantized coefficients.
 161. A decoder configured to: receive a first output signal decoded from a first base encoded stream according to a first codec; receive a level encoded stream; decode the level encoded stream to obtain a set of residuals; and, combine the set of residuals with the first output signal to generate a reconstructed signal, wherein for the decode operation the decoder is configured to: decode a set of quantized coefficients from the level encoded stream; and dequantize the set of quantized coefficients. 